The Modern Data Stack: Does My Company Need One?

A data stack is a type of tech stack designed to facilitate the storage, access, and management of data. Many businesses are adopting a modern data stack to gather, store, transform, and analyze data. Each layer can help you reach your business goals. They enable you to gain insights from the vast amounts of data that you gather during the course of normal operations. This can, in turn, help you to be proactive in discovering opportunities for growth.

The Modern Data Stack, or MDS, differs from legacy technologies. You can get a speedy start with little initial investment in the technology because they often use pay-as-you-go pricing models. An additional benefit of the Modern Data Stack is that you are less likely to experience vendor lock-in. You can choose which vendor to work with for each piece of the stack, mixing and matching to assemble the best toolkit to suit your needs.

Where the Modern Data Stack Comes From

The paradigm shift from traditional methods of data storage in on-premise data centers and other legacy data-management technologies to a more distributed modern approach with key tools that comprise the modern data stack can appear complex and frustrating. This post aims to give you an overview of the roadmap your company would need to follow to implement modern data tooling at scale as you consider a modernized operational strategy. Perhaps more importantly, we’ll start by discussing the merits of the modern data stack to answer the question “Does my Company need a Modern Data Stack?” We won’t keep you in suspense long, because the basic answer to that question is that In most cases, you would benefit from the move to a more modular stack with granular control and centralized storage.

The precursor to this conversation is enlightening and may help you to see why the modern data stack’s evolution was inevitable and valuable. Let’s take a minute to consider the rise of the Modern Data Stack. In recent years an interesting conversation started taking place among data professionals. It sought to answer the reason behind the unbundling of services.

What Do Classified Ads Have To Do With Data Tooling?

This change parallels what we saw in the world of online classified ads when people who once used Craigslist as a centralized place to seek services and goods in classified ads started to make use of more specialized businesses. In effect, modern services sprang up endeavoring to do just one thing really well, and instead of using one site for car buying, ride sharing, dating, and finding houses to buy or rent we now use websites and apps dedicated to each of these services. Gone were the days of the general purpose tool for browns ads. And the behavior of people using these tools reflected a shift in attitudes, smaller, more specialized and modular services were doing a better job of meeting individuals’ needs.

Likewise, while the modern version of data tooling is far more powerful, the core user experience is very similar. If you’re familiar with Craigslist, then Zillow, Uber, AirBNB and Tinder might also feel quite familiar. If you’re familiar with legacy data, DBT, Snowflake, Airflow, Meltano, and others will also feel like parts of what you have seen before.

The Great Unbundling

So, we’ve established that data services became unbundled, but just how did data tools become a suite of choices to make? Basically, there is a natural division of functions within the data landscape and each has associated modern tools. At the base you’ll find the ingestion layer, then transformation, storage, BI, and operational analytics. Some stacks still make use of orchestration tools and other businesses choose to forego those.

The most obvious benefit of this is that companies can now adapt their stacks to a variety of operational needs. Stacks, by their nature, are highly customizable. Most of these tools are uniquely focused on the needs of data engineers and data analysts. A possible downside is that engineers and teams easily toss around specifics that can be confusing to those unfamiliar with the concepts and developing an understanding of your business’s data needs may seem daunting. A data consulting agency like ours is well-positioned to help you make the right choices for your use case.

Does this feel like Patchwork?

The Modern Data Stack is not meant to be a confusing system of interconnected tools, though on the outset it can be. Instead, it is highly customizable and tailored to suit your organization’s unique needs. However, we’ll provide a general bird’s eye view model you can use to understand the way all these tools fit together. This assortment of tools we refer to as the Modern Data Stack allows you to choose what works best for you and build something customized where there was once a monolithic, one size fits all system that purported to take care of all your data needs.

This is where enlisting the help of a seasoned data professional can really be helpful. Someone who understands the various ways a company can put data to use and can envision the best ways to divide responsibility for using different parts of the modern data stack can cut through a lot of the confusion. Teams without guidance can experience many false starts and potentially waste time and money as they proceed to assemble their stacks through trial and error.

Modern Data Stack Model (source)

How the Modern Data Stack (MDS) differs from the Traditional Data Stack (TDS)

Instead of a monolithic application, think of the Modern Data Stack as a layered platform. The bottom layer represents your data sources which might be applications like Salesforce and Google Analytics, databases such as Postgres, Oracle and SQL Server, and files such as spreadsheets, XML, or JSON.

Next would be the ingestion layer which extracts data from the various data sources. This is where data engineers set up automated pipelines using tools such as Fivetran, Stitch, or Segment. There is also an open source integration engine that can work in this layer, Airbyte. When you do this well, you are setting your team up to work with the freshest data available.

After that, there is a storage layer that might include cloud data warehouses such as Snowflake and Amazon Redshift, and/or data lakes such as Databricks and Amazon S3.

The transformation layer is where you can clean raw data in order to facilitate subsequent analysis. You can also make changes to the form it takes to enable use in other tools. Example tools for transformation include DBT (Data Build Tools), which is a SQL command-line program that allows data analysts and engineers to transform data, or Matillion. Both of these are purpose-built solutions for cloud data warehouses.

The operations layer includes tools such as Apache Airflow. Apache Airflow is an open-source workflow management platform for data engineering pipelines. You could also use Atlan, which connects to your storage layer and assists your data teams with providing access to internal and external data and automates repetitive tasks.

Another layer is the analytics layer. This is where you create dashboards and visualizations with tools such as Looker, Zoho, PowerBI, Metabase, and Tableau. You’ll also see tools here for SQL query, and machine learning modeling tools such as Dataiku. Some even parse out a layer called operational analytics (sometimes referred to as reverse ETL) as seen in tools like Hightouch and Census.

Data as a Service

The Modern Data Stack is also related to the concept of “data as a service”. Data as a service, or DaaS, is basically any cloud-based software tool used for working with data. All of these tools are built and ran as a Software as a Service, or SaaS model.

Factors to Consider As You Decide

Once you’ve decided on the main components of your stack, you might want to consider if an open source solution will work. While some companies have expressed preference for tools that are not open source out of concerns for security, our team can help you assess the merits of such a tool, smart implementation, and even guide you through a plan to self-host, if that’s a requirement for your business needs.

Another hot topic: data governance. The Modern Data Stack goes hand in hand with Modern Data Governance. You should understand that good data governance ensures that data is accessible, reliable, available and of high quality. At the same time it supports data security and privacy compliance. Data governance is not just something that is nice to have, it has become a corporate necessity. With the advent of compliance and data privacy regulations such as GDPR and CCPA, businesses must take this into account. A Modern Data Stack can help you comply with regulations in a more agile way.

Outcomes: What Can You Expect After Adopting a Modern Data Stack?

With a modern data stack, you can save time, effort, and money. Your organization will benefit from tooling that is faster, more scalable, and more accessible. If you want your business to transition into a data-driven organization, an MDS can help you reach your goals. We’re here to help you. Doing it right is critical for creating business solutions that solve the right problems and don’t create more problems. Today’s businesses must have actionable, reliable, and up-to-date data to remain competitive. Our data team is ready to help you make the move to a Modern Data Stack.

How to Collaborate with Freelance Data Scientists and Data Engineers

The Face of Freelancing Today

The growing freelance workforce is changing the way companies do business. No longer are businesses completely reliant on full-time staff to get the work done. Instead, businesses have the option of employing a freelance workforce to get the job done. As the freelance workforce continues to grow, businesses are finding that there are many benefits to using a freelance team. We make it easy to get freelance data science work done.

There are benefits from the freelancer’s point of view, too. In fact, 86 percent of freelance talent has opted into the freelance workforce. Some of the reasons they cite for this choice include more flexibility, more control over career development, additional income, and a general interest in participating in the market as an entrepreneurial freelancer. 

From the organizational perspective, one of the benefits of using a freelance team is that businesses can get access to specialized skills and expertise that they may not have in-house. For example, if a business needs a data scientist to help them with a project, they can go to a freelance marketplace and find one. This is a great solution for businesses because it allows them to get the skills and expertise they need without having to hire a full-time employee.

Nation 1099 asked freelancers how they started and why they chose the freelancing route

Another benefit of using a freelance team is that businesses can save money. When businesses hire a full-time employee, they have to pay for benefits, such as health insurance and retirement savings, and they also have to pay the employee’s salary. When businesses use a freelance team, they do not have to pay for benefits, and they only have to pay for the services that the freelancers provide. This can be a significant savings for businesses.

Freelance Data Scientists

Global freelancers are a highly educated group and provide a great value to businesses. Freelance data science professionals are no exception. If you’re looking to grow your freelance team, it’s important to understand how to work with data scientists.

Data scientists are in high demand, and companies are turning to freelancers to fill gaps in their data science teams. But working with data scientists can be tricky. Here are four tips for collaborating with data scientists to grow your freelance team.

1. Start by understanding their skills.

Data scientists are experts at transforming data into insights. They use their knowledge of statistics, machine learning, and data visualization to help turn data into knowledge that can be used to make better decisions.

If you want to work with data scientists, start by understanding their skills and what they can offer your business. This will help you better understand what projects they would be a good fit for and how you can work together to achieve your goals.

2. Give them clear instructions.

Freelance data science team members need clear instructions in order to be effective. When working with them, be sure to provide as much detail as possible about the project you want them to work on. This will help them understand what you need and avoid any confusion.

3. Be patient.

Freelance or not, data scientists can take time to produce results. When working with them, be patient and allow them enough time to complete the project. This will help ensure that you get the best results possible.

4. Formalize Communication. 

Communicate with data scientists through a project management tool such as Asana, Trello, or Jira. This will help you keep track of what tasks have been completed, what tasks are in progress, and what tasks still need to be completed.

Tips For Working with Data Science Freelancers

It’s also important to be clear about your expectations. Make sure you understand the data scientists’ turnaround time. 

When it comes to data science, there’s no question that the freelance workforce is booming. In a recent study, it was found that the number of data scientists working independently has more than doubled in the past three years.

So what’s behind this surge in freelance data science? There are a few factors at work.

First, data science is a complex field, and businesses are often hesitant to hire a full-time data scientist until they’re sure they can make use of their skills. With the help of a freelance data scientist, businesses can get a trial period of sorts, to see how well the data scientist can help them achieve their goals.

Second, the demand for data science skills is high, and there’s a shortage of qualified data scientists. This means that businesses can often find high-quality freelance data scientists at a lower cost than they would be able to hire a full-time employee.

Finally, the tools and resources for working with data are becoming more accessible, which is making it easier for businesses to work with data scientists remotely.

Freelance data science professionals can work on distributed teams (Image Source)

Freelance Data Engineers

Data Engineers work with the same raw material (data), but come with a distinct set of skills. Data engineers in the freelance market are becoming more popular and in-demand as data becomes more complex. In order to find the best data engineer for your freelance team, it’s important to understand the different skills required for the job and what to look for in a data engineer’s profile.

Data engineers are responsible for taking data from all different sources and turning it into something that can be used by the business. They work with big data and create data models to help make better business decisions.

In order to collaborate with data scientists and grow your freelance team, you should look for data engineers with the following skills:

1. Programming Skills

Data engineers need to be able to write code in order to transform data. They need to be able to work with a variety of programming languages, such as Python, Java, and Scala.

2. Strong Math Skills

Data engineers need to be able to understand and work with complex mathematical concepts. They need to be able to create algorithms and models to help turn data into information.

3. Experience with Big Data

Data engineers need to be able to work with large data sets. They need to be able to understand how to store and process data in a way that is efficient and scalable.

How Can a Consulting Agency Bring Value to Your Work?

There are a few things that a consulting agency can bring to your work to help you grow your freelance team. First, an agency can help you find the best data scientists for your project. They have a large pool of resources to draw from and can help you find the perfect fit for your team. Second, an agency can help you manage your data scientists. They can help you create a plan for your project and make sure that your data scientists are staying on track. Lastly, an agency can help you learn from your data scientists. They can help you understand the data that your team is producing and use that data to make decisions about your project.

Leverage Talent

The great resignation signaled problems for some organizations, unwilling to change with the times. However, an agile organization can break away from traditional ideas about who works where and when. This is the time to consider how the great resignation could present opportunities for your team to leverage freelance talent.

According to a study by Upwork and the Freelancers Union, freelancers are now the majority of the American workforce. The study found that 57 million Americans, or 36 percent of the workforce, are freelancers. This number is only going to grow, and some experts say that 50 percent of the American workforce may be participating in freelance work in one way or another in the near future.

If your team is looking to tap into this growing workforce, there are a few things to keep in mind. First, you need to be open to hiring talent from a variety of backgrounds and disciplines. Second, you need to be willing to let go of some control and trust your team to work independently. Finally, you need to be prepared to give your team the tools and resources they need to be successful.

If you can embrace these changes, you’ll be able to find the best talent for your team, no matter where they are located. And you’ll be able to do it quickly and easily, without the need for a formal interview process.

A Statistical Picture of the Freelance Economy

Freelance work is becoming an increasingly important part of the U.S. economy. In fact, according to a recent study by Upwork and the Freelancers Union, nearly 54 million Americans (36 percent of the workforce) are now freelancing.

The freelance workforce is also becoming more diverse, with people from all backgrounds choosing to freelance. This is especially true for women and minorities, who are often underrepresented in the traditional workforce.

Advances in technology have been a big part of what’s driving the shift to freelance work because they have made it easier for people to work remotely. But it’s also being driven by the need for businesses to become more nimble and respond to changes in the marketplace.

Freelance work can be a great way for businesses to get access to high-quality talent without having to commit to a full-time employee. And it can also help businesses to save money on things like benefits and office space.

For all the benefits, managing a freelance workforce can also prove to be a challenge. Use data to identify the best freelancers for the job. Data-driven decision-making will guide you to take more effective steps in realizing your business objectives:

When you’re looking to hire a freelancer, it’s important to use data to identify the best candidates for the job. This can include things like data on past work performance, skills, and even reviews with qualitative notes about how pleasant the freelancer was to work with.

The Onboarding Process for Freelancers

The onboarding process is such an important factor in the success of a good freelance-utilization strategy that we should investigate best practices a bit further. A good quality onboarding process when working with freelance talent should include the following:

1. Introduction

The introduction should include a welcome message, an overview of the company’s mission, and a birds-eye-view of what the freelancer can expect during the onboarding process.

2. Company Policies

The company policies should be clearly explained to the freelancer. This includes information about the company’s expectations, standards and rules.

3. Employee Handbook

Ideally, an employee handbook should be provided to the freelancer. This will outline the company’s expectations and standards in more detail.

4. Training

The freelancer should be given access to any training materials they may need. This will help them to understand the company’s processes and procedures.

5. Resources

The freelancer should be given access to all necessary resources as defined by your company’s operational strategy. This might include physical resources, such as computers, software and phone lines or ephemeral resource keys, like passwords to important applications and subscriptions. 

6. Support

Give freelance talent access to support services where appropriate. This could include help with paperwork, training or any other questions or concerns the freelancer may have. in the long run.

Creating a process for onboarding freelancers will ensure that you properly integrate freelance talent into your company and its culture. It will also help you to get the most out of their skills and expertise.

A Roadmap for Excellent Freelance Onramps

Follow these steps as freelancers ramp up to start working on assigned tasks:

1. Review the freelancer’s profile and credentials.

Make sure that you have a good understanding of the freelancer’s skills and experience. This will help you to match them with the right project or task.

2. Introduce the freelancer to the team.

Make sure to introduce each new freelancer to the rest of the team. This will help them to feel welcome and part of the team.

3. Assign a mentor.

Assign a mentor to the freelancer. This will help them to get up to speed quickly and to learn about the company’s culture and processes.

4. Give the freelancer a project to work on.

Make sure that the freelancer is given a project to work on. This will help them to get started quickly and to learn more about the company and its culture.

5. Monitor their progress and provide feedback as necessary

6. Complete a final evaluation

7. Offer continued support as needed

Avoid Micromanaging

If you are managing a remote team, it is important to avoid micromanaging. This will only frustrate your team and make them less productive. Freelancers need autonomy in order to be productive and creative. Instead, trust them to do their jobs and check in on them occasionally to ensure they are on track.

Give freelancers excellent internal documentation and/or a freelancer community in which they can find answers for themselves. This will help to empower them and minimize the need for micromanagement.

The Freelance data science workforce is growing rapidly and will likely make up 43% of the workforce by 2020. As a result, it’s important to have systems and protocols in place to manage this growing population of workers. If you figure out how your company can manage an increasingly distributed team now, you are setting yourself up for success in the future!

How Can Data Sleek Help You?

We are a data consulting agency specializing in providing the following services:

Data Science

Data Engineering

Analytics

Data Warehousing

Data Architecture

Our team of highly educated freelance data science professionals will work with you to develop the most performant data systems possible. In order to ensure that our clients get the highest quality collaboration opportunities, we maintain a high standard in determining which data professionals can represent our team.

It’s true: companies are getting a great deal when they work with freelance talent. But rest assured, the benefit isn’t just for the company. Statistics show that freelancers are happier and wealthier so you can feel good about the partnership you’re entering. 

OLTP-OLAP Unique Engine: The Best of Both Worlds

The Race is On!

Moving data can be expensive. Especially if it becomes a part of routine business operations. Moving rows between OLTP and OLAP is no different. You generate expenses especially when you generate a lot of transactions per day. The race to unify the OLTP and OLAP engines is on. Maybe you’re wondering if there is one best solution to adapt. OLTP-OLAP to the rescue. An overview of the technologies that attempt to overcome data silo limitations will help you understand the scope of the problem. Is there one engine to rule them all?

In this post, we will take a look at the main differences between OLTP and OLAP. We’ll also explore the goal of new tools available. In doing so, we’ll also chronicle the journey of a savvy business intelligence team looking for their perfect solution. Let’s start exploring how to address enterprise-level transactional and analytical needs. 

Technologies that attempt to merge functions of OLTP and OLAP are also sometimes called HTAP (Hybrid transaction/analytical processing).

Just a few months ago, Snowflake announced Unistore, a new workload for transactional and analytical data. Unistore is exciting news in the data world because adds another tool to the arsenal built for dismantling data silos. Traditionally, we store transactional and analytical data separately. Unistore enables agile data access at scale. 

Using SingleStore is another approach to the problems silos create when we treat transactional and analytical data differently. SingleStore is a distributed SQL database for data-intensive applications. SingleStore already supports OLTP and OLAP analytics on the same database. This allows it to perform transactions and also provide analytics in real time. If you add DBT to the stack, you can then transform OLTP data into an OLAP system. This, in turn, allows you to run reports on the same database where all your transactions are running.

One of the reservations we’ve seen expressed by data professionals is “I don’t need to learn this new technology. I can accomplish the same thing with MySQL.” And that is true to a certain extent. However, when the traffic reaches a certain level, being able to handle transactions and run aggregate queries simultaneously, on the same host becomes an operational issue. Some commonly performed operations can become costly. Think of all the “select count, sum, min, max and group by” statements. Those are not cheap. The cost grows in a way you may not expect because the MySQL engine is not meant to do reporting. It needs to work overtime, racking up computational expenses.

You could work around this challenge by creating a replica where it’s possible to run some queries more efficiently. This tactic works well until you start seeing steady, heavy and uninterrupted traffic and you start needing more of the expensive aggregate queries mentioned above.

The next logical step in addressing these challenges is to consider adding multiple replicas and a load balancer.At this point, your infrastructure cost starts to add up.

That’s all bad news, but if your primary MySQL server fails at any point, that presents another problem. This is a problem you can mitigate by having a stand-by on hand, but that’s yet another expense. Even if you’ve taken all of these measures, you can expect that primary server’s failure to result in a 5 to 10 minute downtime. 

If you’ve chosen to use AWS Aurora, these challenges are less common. Aurora’s parallel processing is AWS’s attempt to catch up with the functionality Snowflake provides. Its caching mechanism is better than what you’re working with in MySQL but the performance, on even the fastest queries, is still not comparable to SingleStore. Besides this, you cannot scale compute the same way as Snowflake does, with a simple select statement. 

Snowflake went the extra mile to support PK (primary key) and Foreign Keys (FK). The canny observer may wonder, based on this feature, if e-commerce Vendors like Shopify will start moving their OLTP on Snowflake or SingleStore in order to provide near real time analytics without having to move data between servers.

SingleStore’s column store engine has supported both OLTP and OLAP since 2019 and  Snowflake just released its Hybrid table. SingleStore supports memory engine, too, for super fast ingestion and can cache data as well. The question remains: Is Snowflake late to the party?

SingleStore might have another advantage to consider in this comparison: It supports MySQL protocol. Because of this, moving an existing app from MySQL to SingleStore is pretty straightforward. It will be a more difficult task to move your app’s data to Snowflake.

OLAP Vs. OLTP: What Are the Key Differences?

People often confuse these two terms for one another. What are their key differences and how can a  company evaluate options to help choose the best approach for your situation?

OLTP(Operational Logs Transactional Processing) is a database engine that builds upon business intelligence. OLTP engines can provide answers for specific, rigidly defined questions.

On the other hand, OLAP (Online Analytical Processing) is a flexibility-optimized database engine. Its best application is to answer higher-level questions in milliseconds.

OLTP-OLAP system design

Simply put, the purpose of OLTP is to manage transactions and OLAP supports decision-making. Transactions are typically generated by a system that interacts with customers or employees. For example, a customer may purchase an item from an online store. The OLTP system would record the purchase, update the inventory, and update the customer’s account.

An OLAP system can help you understand customer behavior. For example, the OLAP system might show how many items a customer has purchased in the past, what items they have purchased, and how much they have spent. This information can help the company understand what products to offer the customer and how to market them.

If you need to do reporting, another tool like Pentaho might be something worth considering. Pentaho can use MySQL as an integration data source.

The main difference between OLAP and OLTP as technologies is the way they process data. OLAP is designed for analysis of data, while OLTP is designed for transaction processing.

OLAP typically uses a multi-dimensional data model, which allows for quick analysis of data by slicing and dicing it in different ways. OLTP typically uses a tabular data model, which is better suited for centralized online transaction processing.

OLTP vs. OLAP characteristics

Enhanced performance and high availability are the key benefits of using a dedicated OLTP-OLAP unique engine. By separating OLTP and OLAP operations, you can improve performance, ensuring that the data required for OLAP processing is always available. You can also use a clustered file system or a load balancer to improve performance and high availability.

OLTP-OLAP Unique Engine is a new type of database that combines the best of both worlds: the performance and scalability of an OLTP database with the flexibility and querying power of an OLAP database. Unique Engine is designed for businesses that need to run fast, multi-dimensional queries on large amounts of data. It offers the performance and scalability of an OLTP database, while also providing the flexibility and querying power of an OLAP database.

Snowflake is currently the only cloud data warehousing solution that supports both OLTP (online transaction processing) and OLAP (online analytical processing) workloads in a single system. This unique architecture delivers the best of both worlds: the performance, scalability, and flexibility of a cloud data warehouse for your OLTP workloads, and the ease of use and fast performance of a traditional data warehouse for your OLAP workloads.

It is a common goal for database vendors to unify the OLTP and OLAP engines into a single platform. After all, this would seem to offer the best of both worlds – the performance and scalability of OLTP together with the flexibility and power of OLAP.

However, there are good reasons to question whether this is the right goal. Firstly, the OLTP and OLAP engines are actually quite different in their nature and purpose. OLTP focuses on transactional processing, while OLAP is focused on data analysis. They are two very different workloads, and trying to merge them into a single platform may not always be the best solution.

Secondly, unifying the engines can actually lead to a loss of performance and scalability. When you combine the OLTP and OLAP engines, the platform becomes more complex and the overhead of managing the system increases. This can lead to a decline in performance and scalability. However, it may end up being a better solution than other workarounds we’ve already explored.

So is it really worth trying to unify the OLTP and OLAP engines? In many cases, the answer is no. There are good reasons some organizations might choose to maintain two separate engines, each suited to a particular purpose.

OLTP-OLAP Unique Engine is an innovative approach to database design that combines the best of both OLTP and OLAP systems in one system that is both operational and analytical in nature.

OLTP systems provide fast, reliable transaction processing, while OLAP systems get you fast, efficient analysis of data. Traditionally, these two types of systems have been separate and distinct, with different architectures and data models.

The OLTP-OLAP Unique Engine has the following features:

– Fast, reliable transaction processing

– Fast, efficient analysis of data

– Flexible data model that supports both OLTP and OLAP operations

– Efficient use of disk space

– Scalability to accommodate large amounts of data

Data Replication and Partitioning

SAP HANA is another unique solution that offers an engine to handle both Online Transaction Processing and Online Analytical Processing workloads. SAP HANA can also handle data replication and partitioning. The unique engine is a key part of the OLTP-OLAP system. It is responsible for managing the data in the system, and it manages the interaction between the OLTP and OLAP systems.

The unique engine can also be described as a distributed system that runs on a cluster of servers. It is designed to be scalable, so it can handle large amounts of data. The unique engine is also fault-tolerant, so it can handle failures of individual servers. In terms of implementation, it is designed as a set of Java servlets.

Historical analysis of cloud observability data is one use-case focused on analyzing cloud observability data, initially gathered as operational data, to improve the understanding of past performance and to help identify issues before they become problems.

The first step is to gather data from all of the relevant sources. This includes data from the cloud provider, data from monitoring systems, and data from other sources such as log files. The data is then pre-processed to clean it up and to make it ready for analysis.

The next step is to analyze the data to identify trends and patterns. This can include analysis of time series data, correlation analysis, and other types of analysis.

The final step is to use the results of the analysis to improve the understanding of past performance and to help identify issues before they become problems. This can include creating reports, dashboards, and other types of visualizations.

Companies can use this data orchestration to analyze the performance of their cloud services in order to improve customer experience. The company could track how well their services respond to changes in load and usage and potentially identify any issues before they cause customer complaints. Additionally, the company could use the data to investigate the causes of outages and other performance issues in order to fix them and prevent them from happening again in the future.

We Can Help You Start Optimizing Your Data Usage

Clearly, there are a lot of options to sort through. OLTP-OLAP Unique Engine is a revolutionary new technology that enables you to get the most out of your data. With OLTP-OLAP Unique Engine, you can easily and quickly create a unified view of your data that combines the best of both worlds – the speed of OLTP and the flexibility of OLAP. From a business perspective, it means you can make just in time decisions using all of the data available to you. From a technical standpoint, this translates to a more stable system with less need to devote engineering time to keeping the system up to date and working. It also means that computations are more efficient. 

OLTP-OLAP Unique Engine is the perfect solution for organizations that need to quickly and easily analyze high volumes of data. This unique combination enables you to quickly and easily analyze your data, and get the insights you need to make informed decisions. It is also the perfect solution for organizations that need to scale their data analysis capabilities. With OLTP-OLAP Unique Engine, you can easily add new users and new data to your system, with very little complexity added.
Start implementing an efficient and powerful tool today. You can reap the rewards of OLAP and OLTP efficiently, and see the benefits for yourself! Data-Sleek’s team of data professionals can help you implement the right tool for your needs, saving you from considerable detours in the process of discovery and saving you time and money in your quest for on-target analysis.

What are the Advantages of Building a Data Warehouse In the Cloud?

Many organizations are taking on the task of modernization in terms of how they set up systems to make use of data. In the past, different teams within the organization may have independently managed the life-cycle of data, but that resulted in siloed information. In an age where data is practically synonymous with currency, it makes sense to pool information from teams within organizations to build better intelligence. After all, good data is the basis of great machine learning. There are a few advantages of building a data warehouse in the cloud:

1. Reduced Costs – One of the primary advantages of using a cloud-based data warehouse is the reduced cost. With a cloud based system, businesses can avoid the cost and complexity of deploying and managing their own data warehouse infrastructure.

2. Increased Flexibility and Scalability – A cloud-based data warehouse can also be scaled up or down quickly to meet the needs of the business. This flexibility can help businesses avoid the need to invest in excess capacity, which can be expensive and difficult to scale.

3. Increased Security – Another advantage of using a cloud-based data warehouse is the increased security. Also, with the cloud, businesses can rely on the security features offered by the provider, including data encryption, firewalls, and intrusion detection.

Data Warehouse Defined

A data warehouse, sometimes referred to as a cloud warehouse, is a repository used to collect and store data from disparate sources within an organization. Through orchestration the process happens automatically, the data is cleansed, standardized, and integrated before it is made available to business users for analysis.

Source

This means that all operational tools can become sources of information to inform business decisions at a macro level. More complete data translates to better decision-making power. 

Cloud Data Warehouse

This technology that allows you to store and query data in the cloud.

A cloud data warehouse is basically a technology that allows you to store and query data in the cloud. This can be a great option if you’re looking to reduce your on-premises hardware requirements, or if you want to take advantage of the scalability and elasticity of the cloud.

When evaluating a cloud data warehouse, you’ll want to consider the following aspects of your data:

Volume – How much data do you need to store?

Variety – How diverse is your data?

Location – Where is your data located?

Processing – How much data needs to be processed?

Querying – How often do you need to query your data?

Cost – What’s the cost of using a cloud data warehouse?

Why It Matters

The cloud warehouse is a new technology that is becoming more popular. It allows companies to store data in the cloud, which makes it easier to access and share. This can be useful for companies that need to store a lot of data or need to be able to access it quickly.

In cases that could be classified as big data organizations, the ability to perform parallel processing becomes very important. Using parallel parallelism, vast quantities of data can be processed in minutes, not hours or days. This can be done using multiple processes to accomplish a single task, but not all data warehouses are set up to enable this kind of work. This is dependent on the cloud data warehouse architecture, which often dictates what kinds of processes you can apply to the data.

Comparison Guide: Top Cloud Data Warehouses

When it comes to data warehouses, the cloud is the new frontier. Cloud data warehouses are growing in popularity for a variety of reasons, including the ability to quickly spin up new instances, the scalability to handle large amounts of data, and the pay-as-you-go pricing model that eliminates the need for capital expenditure.

If you’re considering a cloud data warehouse, it’s important to understand the different options available. This guide provides a comparison of the top cloud data warehouses on the market today.

Top 6 Data Warehouses and Best Picks for a Modern Data Stack

There are a few different cloud data warehouse providers on the market. They all offer different features, and cloud data warehouse architecture can vary widely, so it can be tough to decide which one is the best for your needs.

Here is a comparison guide of the top cloud data warehouse providers:

Each technology has its own advantages and disadvantages. Here is a comparison of the three technologies:

AWS Redshift

Amazon Redshift is a popular cloud data warehouse provider. It offers fast performance and scalability, making it a good choice for large datasets. It also has a variety of integrations with other AWS services, making it easy to start. Amazon Redshift is one of Amazon’s data warehouse services. It is designed to handle large-scale data analysis and querying.

Google BigQuery

Google BigQuery is another popular cloud data warehouse provider. This cloud data warehouse stands out because it offers high-performance, speed, and scalability, as well as a variety of integrations with other Google services. It also has a low price point, making it a good choice for budget-conscious businesses. Google BigQuery is a cloud-based data warehouse and analytics platform developed by Google. It allows users to run SQL-like queries against very large datasets.

Snowflake

Snowflake is a newer cloud data warehouse provider that is quickly gaining popularity. It offers fast performance, scalability, and a variety of integrations. It also has a low price point, making it a good choice for budget-conscious businesses.

Apache Hive:

Apache Hive is a data warehouse system for Hadoop that facilitates easy data summarization, querying, and analysis.

Frequently Asked Questions

So, how does the data get into the warehouse?

Generally, pipeline, orchestration, and operational tools build architectures for managing the movement of data from collection point to the cloud warehouse. Often, part of the process of moving this data is transformation, so ETL is an important concept to delve into as you start moving operational data into the centralized cloud warehouse.

Considerations for a data warehousing provider

There are a few different options for a data warehousing provider. Amazon Web Services (AWS) amazon redshift, is a popular option, as is Microsoft Azure. Other providers include Google Cloud Platform, Rackspace, and IBM. Data Warehouses can be used to operationalize data

in a number of ways. The most common way to operationalize a data warehouse is through the use of a data mart.

What is the most common way to operationalize a data warehouse?

Following best practices in ETL

(Extract, Transform, Load) methodology, data is extracted from a data source, cleaned and transformed into the desired format, and then loaded into a target data store.

A cloud warehouse is a type of warehouse that is designed to take advantage of cloud computing technology. Cloud warehouses use cloud-based software and services to manage and store inventory, process orders, and track shipments. This allows businesses to reduce their IT infrastructure costs and improve their efficiency.

Source

The most common way to operationalize a data warehouse is through the use of a data mart. Informs machine learning with data from your warehouse

What is a data warehouse?

A data warehouse is a repository for data businesses organize in a way that makes it easy to use for analysis. The data in a data warehouse is typically extracted from multiple sources, such as transaction systems and marketing databases.

A data warehouse is a system for storing data extracted from different sources in order to support decision-making. The data is organized in a way that makes it easy to find and analyze.

There are many reasons why you might need a data warehouse. For example, if you want to track customer behavior across different channels, or if you need to consolidate data from multiple sources in order to perform a statistical analysis, you would need a data warehouse.

If you’re not sure whether you need a data warehouse, consider whether you need to

  • consolidate data from multiple sources
  • track customer behavior across different channels
  • perform a statistical analysis
  • store data for a long period of time
  • access data in real time

If you answered yes to any of these questions, you might need a data warehouse.

Data silos are a common challenge in warehousing. Each department or team may have their own data, collected and managed in their own way. This can lead to inefficiencies and data duplication. A data warehouse can help to consolidate this data, making it easier to access and use.

A data warehouse can also help to improve data quality. By consolidating data from multiple sources, the data warehouse can identify and correct inconsistencies. This can help to improve decision-making and analytics.

Cloud Data Warehouse Automation

Automation can help organizations to significantly speed up the deployment of their data warehouse and improve the reliability and efficiency of their data warehouse operations.

There are a number of different automation tools and technologies you can use to automate the deployment and operation of a cloud data warehouse. Some of the most common automation tools include:

Source

– Puppet

– Chef

– Ansible

– Salt

– Jenkins Cloud data warehouse automation is the use of cloud-based technologies to manage and automate the operation of a data warehouse. Automation can include the use of cloud-based tools to provision and manage data warehouse resources, as well as to automate the processes of data loading, transformation, and analysis.

Cloud-native data warehouse automation can enhance your capabilities and improve the efficiency and reliability of data warehouse operations. They can also ensure proper utilization of data warehouse resources. Automation can also help to improve the quality of data warehouse output, and can make it easier to manage and monitor data warehouse operations.

Cloud Data Warehouse Architectures

Data Warehouse Architecture - GeeksforGeeks

Businesses often use cloud data warehouses to store data from a variety of sources, including data from internal systems, data from customer interactions, and data from social media.

Use cloud data warehouses to store data in a variety of formats, including structured data, semi-structured data, and unstructured data. This makes it possible to store data from a variety of sources in a single location, which can make it easier to analyze.

You can use cloud data warehouses to store data in a variety of ways, including storing data in a way that makes it easy to query and analyze, simple to replicate and share, quick to export, and possible to combine with data from other sources.

Take the Next Step

Data Warehouse Overview - Data Warehouse Tutorial | Intellipaat.com

Now that you understand the basics of the cloud warehouse, it’s time to take the next step with your own purpose-built solution. Once you’ve decided you want to move forward, you should check out our data warehousing services to learn how we can help you start. In addition, if you’re looking for specific applications or services, be sure to check out some of our case studies, where we have successfully integrated cloud warehousing for improved business operations. 

The 6 Steps You Need to Take to Begin Securing Your SaaS Business

Your SaaS business isn’t immune to data breaches, but you can take steps to keep your customers’ data safe from the hands of cybercriminals. If you are looking for ways to improve your SaaS security and protect yourself from potential threats, these six steps will get you started on the right path. Make sure that all of your employees – from entry-level staff to C-level executives – know about these critical security measures so they can stay vigilant at work and out in the field.

1.Assess the Threats

The first step to take in securing your SaaS business is to assess the threats. What are the potential risks and what could happen if they materialize? Identifying the risks will help you prioritize the security measures you need to put in place and having a plan of action to follow in the event of a breach will help you react quicker.

2.Implement User Account Controls

User account controls are the first line of defense against unauthorized access to sensitive data. By requiring users to authenticate themselves before accessing data, you can ensure that only authorized users can access it. Implementing user account controls can be as simple as requiring a unique user name and a strong password to login, or you can use more advanced methods like multi-factor authentication (MFA). Multi-factor authentication is certainly the better way to protect your SaaS application, as long as you also disable legacy protocols that hackers can use to get around your MFA requirements.

3.Use a robust hosting provider

A hosting provider that offers robust security features and guarantees high availability is essential for any SaaS business. Not only will this ensure that your data is safe, but it will also give you peace of mind knowing that your customers can always access your service. This type of provider typically has three main types of safeguards: SSL encryption, DDoS prevention, and malware detection and removal.

4.Encrypt your data

Data encryption is one of the most important steps you can take to secure your SaaS business. By encrypting your data, you make it much more difficult for hackers to access and misuse your customers’ sensitive information. Additionally, encrypting your data can help you comply with data privacy regulations, such as the EU’s General Data Protection Regulation (GDPR).

5.Educate your users on security

No matter how secure your system is, data breaches can still occur if your users don’t know how to keep their data safe. Educate your customers on best practices for security, such as using strong passwords and keeping their software up to date. You can also provide resources, such as a security blog or FAQ section on your website. Additionally, include security in your onboarding process so that new customers are aware of best practices from the beginning. Lastly, use tools like 1Password or LastPass that can generate strong passwords for users and even let them sign into websites with one click.

6.Keep an eye out for suspicious user activity

One of the best ways to keep your SaaS business secure is to monitor for anomalous user activity. This means keeping an eye out for things like password spraying and excessive failures. Another great way to keep your SaaS account safe is by monitoring for compromised accounts in threat intelligence feeds. Threat intelligence platforms are designed to aggregate data from across different sources so that they may be monitored or used proactively. Compromised accounts could include databases or credentials that would allow hackers access to a system. By doing this, you can quickly identify and address any potential security threats before they become an issue.

Conclusion

Although each one of these measures alone may not be enough to provide complete protection against every threat on the internet, they are a great start!

If you are ready to level up your approach to data security, consider hiring a data security or a data management firm. This will ensure that all of the steps of the process are carried out in an accurate and systematic manner. Data breaches can be costly and cause irreparable damage to your company’s reputation, so if you want to protect your business and make sure it is compliant with all relevant laws, then look no further than a professional data management company. 

At Data Sleek, we help SaaS providers optimize their databases, ensure data security, and streamline data into insights. We’d love to assess your application and see how we can help you improve both performance and security.

Metabase vs. Tableau: Which BI Tool is Right for You?

With several BI Tools available on the market today, it can be difficult selecting the right one for YOU. When making a selection it is important to not only consider cost but also capabilities and scalability. Below we’ve outlined a comparison between two very popular tools – Tableau and Metabase. If you’re exploring these two tools, hopefully this will help you make an informed decision! 

Advantages of using Metabase vs. Tableau: 

  1. It is fairly simple to use, and learn, compared to Tableau.
  • It as friendly overall user interface
  • it is very easy to join tables on keys 
  1. It has unique features: 

Share reports automatically. You can email reports or dashboards daily / monthly / or after any specified time period directly to an email list.

 Drag and drop visualizations. It is EASIER to drag and drop tables/ visualizations onto dashboards compared to Tableau. If the data is fed correctly through a database, visualizations can be created simply by dragging and dropping.

Creating dashboards is a breeze! A beginner with no experience can easily build dashboards to create visualizations. 

(The tool provides certain suggestions such as aggregates on which visualizations can be built – like calculating the mean of a particular column. For example, Metabase may suggest calculating the mean order amount by customer, and will calculate it for you.) 

Disadvantages of using Metabase vs. Tableau: 

  1. Lack of flexibility

Poor filtering. We tried to create a filter that can filter data based on orders in the last x days and could not accomplish that in Metabase. Instead, we had to create separate visualizations for each of: yesterday, last 90 days, last 60 days, last 30 days. 

No formatting flexibility. We could not add custom labels or change colors. 

  1. Lack of resources

Metabase has a lot fewer tutorials and community support at this point, making it even more unfriendly to beginners.

  1. Highly reliant on SQL

There were many duplicates in the dashboard that we created. It was impossible to filter the duplicates through Metabase. 

  1. Fewer collaboration options

There is no way to share dashboards other than through an email. 

Here’s an at-a-glance look at the pros and cons

Pros

TableauMetabase
Offers a free trialLower price point compared with similar tools
User friendly; easy-to-use UI; simple set up processGreat for beginners, user friendly; easy-to-use UI; 
Extensive analytics & reporting options availableOffers a 14-day free trial
Wide variety of deployment options
24/7 Support available 
Variety of training resources available 
Mobile friendly

Cons

TableauMetabase
Higher price pointLimited analytics; no benchmarking
Poor Versioning Limited deployment options
No customer support available 
Limited training resources
Limited Graphing capabilities
No Desktop version

If you’re looking for a free data visualization tool for some basic graphs and you want to do it yourself using SQL, then Metabase may be a great tool for you. On the other hand, Tableau desktop version costs $70 and will allow you to join data between databases and Excel, customize queries against data and build some very sophisticated graphs with filters – and much more. 

SingleStore vs. ClickHouse Benchmarks

We’re often engaged in consulting projects where we are asked about a range of different database options for scalability, query performance and reliability.

We’re commonly asked about ClickHouse as an option, likely because it’s free and queries are supposedly fast. Although both are true, it’s important to think about scalability, reliability and architecture change, like needing to join several tables. SingleStore is a distributed relational database known for speed, scale and its ability to join several tables. It is suited to many of the same use cases as ClickHoue, so it’s a good comparison.

In my line of consulting work, it’s not enough to offer anecdotes and opinions — the data is necessary to support my observations. Below you’ll find benchmark results against TPCH standard data.

The benchmarks for ClickHouse and SingleStore Cluster In a Box (CIAB) was performed on a 64GB, 8CPU, 200GB SSD Disk (similar to a r5.2xlarge EC2 instance). The dataset was stored on a 250 GB Digital Ocean Volume attached to the droplet. Data was ingested locally from the attached storage using the TPC-H benchmarks files — the largest file being 75GB (lineitem) for 600 MB rows. 

SingleStore vs. ClickHouse Ingestion – 3 points

Table NameTotal RowsTotal File SizeClickHouseSingleStore
customer15,000,0002.3 GB11s22s
lineitem600,037,90275 GB5m 4s11m 38s
nation252.2k0ms0ms
orders150,000,00017GB1m 18s2m 49s
part20,000,0002.3 GB13s24s
partsupp80,000,00012 GB47s1m 34s
region51k0ms0ms
supplier1,000,000137 MB2s3s

Data Loading was done using 8 files  (1 file per table for TPC-H), residing on the attached storage, using a bulk load method (see file at end of article). We did not test ingestion using Singlestore Pipelines which performed better in another test (in AWS). For the large tables, ClickHouse performed much better on the data load, twice as fast for largest tables.

Although data load time is important, it’s not the most critical point. Our main goal was to show case how fast queries are against large tables in ClickHouse Vs Singlestore when using joins.

Note: While ingesting using Load Data Infile in SingleStore, querying the table (select count(*)) does not return records until the load is completed. This result is different when using SingleStore’s Pipelines, which allows you to query the tables as data loaded. The record count will update each time the SingleStore Pipelines commit the batch of records (which can be specified). 

Ingestion Conclusion

When it comes to ingestion, ClickHouse was twice faster on average then SingleStore. Singlestore gets one point because it’s possible to run a query against a table where a large amount of data is being ingested into, nolocking occurring using pipeline.

SingleStore pipeline ingestion is quite powerful. Not only can it connect to S3, Kafka, Azube Blog and HDFS, it can also support various formats including Parquet, CSV, TSV, JSON and more. SingleStore also offers transformational capabilities — Pipelines can be stopped and started again, without losing data. Lastly, because Pipelines are created with SQL, you can dynamically create and start them.   

Points: SingleStore 1.5, ClickHouse 1.5

SingleStore vs. ClickHouse Queries – 3 points

Queries were performed on the same Digital Ocean instance. ClickHouse was installed to first perform the query test, then to perform the shutdown. Then, SingleStore was installed and set up as SingleStore-in-a-box (1 primary aggregator, and 1 leaf node).   

QueryClickhouseSinglestoreSpeed Diff
10s 0ms0s 20ms0
20s 322ms0s 40ms0
37s 727ms2s 960ms3
481s 626ms0s 440ms186
56s 470ms0s 170ms38
66s 359ms0s 710ms9
716s 397ms18s 110ms1
8148s 0ms3s 610ms41
941s 135ms4s 300ms10
1021s 876ms8s 370ms3
11600s 0ms21s 630ms28

Although ClickHouse ingests faster as seen in previous tests, results show that SingleStore clearly outperforms ClickHouse — especially when joining tables. Queries 1, 2 and 3 are simple queries against a single table: lineitem. As you can see, there are no major, notable differences between the two databases. These queries are handled within microsecond differences, which is not noticeable when manually running queries. 

Performance starts to quickly degrade when ClickHouse starts joining tables (query 4 to 11 in the graph). Query 4 (joining 2 tables and doing a limit) takes 440 milliseconds in SingleStore, and 81 seconds in ClickHouse.  Queries 8-11 were actually failing in ClickHouse until we increased the amount of available memory. Additionally, ClickHouse was unable to complete query 11 — even after assigning 50GB of memory. SingleStore completed the query in 21 seconds.

The shorter the bar, the better!

Queries Conclusion

When it comes to queries, ClickHouse can quickly query a single table, with SingleStore closely matching performance. When ClickHouse must join tables, performance degrades considerably. This is why the benchmarks listed on ClickHouse’s website are always against single (flattened) tables.

Points: SingleStore 2.5, ClickHouse 0.5

We would like to give ClickHouse one-half point because queries against single tables are very fast. But that is where ClickHouse performance stops. We have not even tested queries with Common Table Expressions (CTE), which ClickHouse seems to support.

GUI Administration & Monitoring – 3 points

Administering and monitoring your database is critical. Clickhouse has some open source GUI but they seem pretty limited, mostly running SQL select queries. Monitoring is possible via Grafana.

SingleStore comes with SingleStore Studio, which allows you to monitor and get a great overview of the cluster’s overall health: 

  • The dashboard shows Cluster Health, pipeline status, cluster usage and database usage. 
  • It looks at the difference in host CPU consumption, disk space used and how much memory is consumed.
  • Database Metadata: Users can look at each database and dive in to see the stats about each table (total rows, compression, how much memory / disk space is consumed).
  • Active Queries: Similar to “show process list” in MySQL, this allows users to see running queries.
  • Workload Monitoring: You can start workload monitoring which profiles the activities running on a cluster, tracking all queries being executed — and quickly identify those that are most resource intensive.
  • Visual Explain: A query profile can be saved, then loaded, into Visual Explain to see a detailed query plan
  • SQL Editor: One of the most popular features, this allows users to run queries within the browser (just like Snowflake)
  • Pipelines: Shows pipeline running

 Points: SingleStore 2.5, ClickHouse 0.5

Advanced Features – 3 points

SingleStore provides full redundancy out of the box when using a cluster with at least 2 aggregators and 2 leaf nodes. Leaf nodes can use the High Availability feature, allowing data to be copied on each leaf to provide full redundancy. If a leaf goes down, the cluster can still be used. ClickHouse can be used also as a cluster but the implementation, configuration and administration is not as simple as SingleStore.

Stored Procedure

SingleStore supports stored procedures. Pipelines can ingest into stored procedures, allowing you to transform data or maintain aggregates (for example, a materialized view).

S3 Table

Both SingleStore and ClickHouse support S3 as a storage engine, although SingleStore has implemented a more robust solution. In SingleStore, the S3 storage is created at the database level, meaning all tables created in that database will use S3 storage. In ClickHouse, the storage is at the table level. SingleStore has also a memory / disk caching layer for hot data when using S3 storage, enabling great performance. When using S3 as a storage layer for a database, data spills over to S3 if the disk gets full. 

UDF, Time Series, Geospatial, etc.

SingleStore supports many advanced analytical functionalities including JSON extract, time series (time bucket), geospatial functions and more. The SingleStore database is really built for analytics.

PTR (Point-in-Time Recovery)

PTR enables system-of-record capabilities. Customers can now operate their SingleStore databases with the peace of mind that they can go back in time to any specific point, and restore any data lost from user error or failure.  

Points: SingleStore 2.5, ClickHouse 0.5

Cost – 3 points

ClickHouse is free open source software, although there are now some paid options too. SingleStore provides a fully-featured free version for production that you can run up to 4 nodes in any environment you choose. For the cloud, SingleStore provides $500 in free credits if you prefer the managed service. ClickHouse does not provide redundancies — deploying a ClickHouse system in production is risky unless you have an in-house expert standing by. SingleStore’s support is excellent, and they’ll answer questions in their forum (even if you’re not a customer).   [1] [LF2] 

Points: SingleStore 1, ClickHouse 2

Final Conclusion

As DBAs with some Data Engineering experience, we can conclude that SingleStore offers a much stronger solution than ClickHouse. The performance of the queries when joining tables is obvious — queries were 3-186x faster.

In many cases, memory had to be increased using SET max_memory_usage = 40000000000 before running the query or it would fail. ClickHouse is another memory database that seems to heavily rely on scanning rows quickly in memory to generate results. Performance takes a big hit when tables need to be joined, which SingleStore handles without issue.

Furthermore, SingleStore consistently adds new features, improves its admin and monitoring tools and now supports S3 storage.  The number of features available in SingleStore for analytics surpasses those of ClickHouse. SingleStore also supports modern data engineering ingestion, allowing ingestion from Kafka, S3 and more by just using a few lines of SQL code.

Total Points

SingleStoreClickHouse
105

Data Analytics

Data Analytics is the science of analyzing raw data and the process of inspecting, cleansing, transforming, and modeling that data in order to draw conclusions from it. Techniques used for data analytics can reveal trends and metrics that otherwise would be lost in the mass of data. The goal is to discover useful information for informing and supporting decision making. In today’s business world data Analytics is helping businesses operate more effectively.

Types of Data Analytics

Descriptive Analytics

In simplified terms – looking at what has happened in the past. Its purpose is to describe what has happened and its goal is to make the information digestible and usable. Descriptive analytics is a view of data related things like how many visitors have visited a website, what social media posts have garnered the most attention, what blog tools have been most successful, how people opened a particular email … and the list goes on.

Diagnostic Analytics

The “why” behind something happened. Diagnostic Analytics main purpose is to identify and respond to anomalies in your data. For example, there’s a drop in monthly sales in a peak season – you want to know why and what contributed to it. Diagnostics aren’t only for the negatives though! It can also help you identify what is positively contributing to sales. How well things like ads could be working, or influencer marketing, or other things you’re implementing that are making impact.

Predictive Analytics

Like its name – predictive analytics is helping to predict the future. Based on past patterns and trends in data, predictive analytics can help you estimate the likelihood of future events and outcomes. Which can be especially useful for a business looking forward and planning ahead. This can be especially useful for seasonal variables – predicting customer value, or a myriad of other things.

Prescriptive Analytics

What has happened, why it happened, and what might happen next in order to determine what should be next. This helps to determine what next steps can be taken to take advantage of future outcomes that have been predicted. Prescriptive Analytics can help with steps to avoid future problems or capitalize on trends.
The most complex type of Analytics – this involves algorithms, machine learning, statistical methods, and computational modeling procedures, to consider all possible outcomes and pathways a company could take.

Data Visualization

Data visualization is a graphical representation of data using visual aides such as graphs, charts, and maps to provide a streamlined approach to visualize and understand patterns and trends in data. Part of data Analytics is the visual representation of the data. This tells the story of your data. Visualizations of data can make the information easier for the human brain to understand. It is the most efficient way to showcase data for the purposes of management teams who need to be able to quickly identify things like patterns, representations, and other insights from the data. Instead of digging through a pile of analytics to find critical information – data visualization helps expedite the process and help you find the conclusions you need to make business decisions.

Our Data Visualization team consists of business analysts who have a clear understanding of business metrics and tools like Tableau to deliver the data graphic representations critical to you. We are data visualization experts at information graphics and scientific visualization, exploratory data Analytics, and statistical graphing and are aware of the importance of data visualization and we treat data visuals as one-part science and one part art.

We work to make large sets of data coherent and applicable to your business. With the accomplishment of the goal you will have the right data at the right time to make business decisions that affect your bottom-line revenue.

At Data Sleek, we believe that good data visualization is where communication, data science, and design intersect.

Data Science

Data Science is a term that can encompass a lot of different data related services. Some of these you can learn more about under Data Architecture or Data Engineering. Let’s break down why data science is so important and how it can positively impact your business!

Why Data Science is Important For Your Business?

As the amount of business data increases and becomes available, large enterprises and tech companies are not the only ones who can utilize Data Science. Data Science takes large enterprise data models and convert them to suit your specific business and objectives.

Data science methods can make comparisons to competition, analyze markets, explore historical data, with the ultimate goal of giving you the best recommendations of where and when products and services sell best. This can give companies the ability to tailor products as needed, and business practices for best case scenarios.

At Data Sleek, we help small and medium-sized businesses make their entry point into data management and collection.

We will help you make decisions and predictions based on casual and predictive analytics, and machine learning. Strategize the best course of action to make your data work best for you.

When we take you on as a client we will emphasize these key areas of special data analysis:

  • Deep user behavior analysis
  • Predictive insights
  • Product comparisons
  • Product categories
  • Fraud detection

How We Use Data Science To Help you

Better Data Based on Better Analytics
Helps management teams have the best available data that communicates and demonstrates their analytics capabilities to be utilized to improve decision making processes.

Identify Data Opportunities
Question the existent processes and systems with the goal of development and improvement to methods and analytical algorithms.

Target Audiences
Almost all companies collect audience data, via Google analytics, Facebook’s pixel, customer surveys, or some other method. But if not well utilized you could be missing key demographics segments that could be interested in your product or service.

Predictive Causal Analytics
Helps predict the possibilities of a particular event in the future by applying predictive causal analytics.

Prescriptive Analytics
Data models with the intelligence to make their own decisions and the ability to modify it’s parameters.

This is a relatively new field that the Data Science team at Data Sleek is innovating for small and medium-sized businesses.

Machine Learning
Using your transactional data to build models based on future trends using MI algorithms. A paradigm called “supervised learning,” so we teach our machines how to learn. We also use MI for pattern discovery to find new areas of revenue growth for your business. 
At Data Sleek, we take data seriously and want to explore the possibilities with you.

We will present you with recommendations that will positively affect your business decisions. We utilize essential technical tools and skill sets such as:

At Data Sleek, we take data seriously and want to explore the possibilities with you.

Data Engineering

Like any engineer – Data Engineers design and build. In the case of data engineering, what they are building are the pipelines that transport and transform your data into an ideal format for your business needs. Pipelines take data from many disjointed and separate sources and collect them into a Data Lake or a Data warehouse that represents in a uniform way the single source of truth for the enterprise data. All reports depend on the Data Warehouse. Trust is key.

By definition, data engineers use programming languages to build clean, reliable, and repeatable relationships between data sources and databases.
Our engineers focus on the practical application of data collection and analysis for your business.
Our Data Engineers will focus on these three core areas of your business.

System Architecture
Helping to choose the right Data Integration systems or service that will work together in harmony to extract  data sources efficiently and assure data delivery & quality for your business.

Programming
We have expertise with the following Database technologies : Snowflake Computing, MySQL, and SingleStore.
We are experts in Dimensional Modeling, Fivetran, Stitch Data, and other online data services.
Our engineers are proficient in languages like SQL, Python, Java, and Scala.

Analytics
Our staff of engineers will ask the right questions to make sure we build a system that grows with you as your business scales up.

Here at Data Sleek we believe in

Data Integration Services
Combining data from different sources and systems to provide users with a single unified source of truth to provide synchronization of data to be utilized by management teams and decision makers.

Dimensional Modeling Expertise
Understanding the steps necessary to  transform OLTP models into Dimensional Models for efficient reporting.  ( A lot of people don’t know about it at all and iit is becoming increasingly important even in job descriptions). (Dimensional Modeling is part of the Data Warehouse Architecture )
Dimensional modelling in data warehouse creates a schema which is optimized for high performance. It means fewer joins and helps with minimized data redundancy. The dimensional model also helps to boost query performance.

Data Purity – We use data dictionary to match your data properly to its origin. It is fundamental as it will help build the queries later in the data warehouse.
If you’re interested in setting up pipelines between your data source and a data warehouse, how to scale reporting solutions, how to re-architect and scale your data, or need help with real time analytics – let’s talk about the solutions you need!

If you’re interested in setting up pipelines between your data source and a data warehouse, how to scale reporting solutions, how to re-architect and scale your data, or need help with real time analytics – let’s talk about the solutions you need!

Data Architecture that Fits Your Needs

The right database architecture allows businesses to scale painlessly while growing its data infrastructure.

The Purpose of a Data Architecture and why it’s important to your business

Data is everywhere in business – from systems, to departmental databases, spreadsheets, and reports. Often erratic and duplicated across systems, the quality of your data depends on multiple variables. Despite the chaotic quality your data can have it is the core of business – which makes the need for quality architecture more important.

Data Architecture is the process of how you organize, collect, store, and utilize data with the goal to get quality relevant data into the hands of those who need it to make informed business related decisions. A strong architecture enables you to standardize your data helping you make informed decisions for sales, marketing, and forecasting.

Data Architecture are the policies, rules, and models that determine how data gets collected and what kind of data- included and/or transformed for processing and storage. This can include rules governing things such as file systems, databases, and systems that connect data and the business process consuming it. A strong data architecture enables you to standardize your data helping you make informed decisions for sales, marketing, and forecasting down the road.

Symptoms of poor data architecture include:

  • Slow applications, difficulty to scale
  • Latency spikes under load
  • Unscalable databases
  • Code that needs to be refactored
  • Mismanaged data models

Mismanaged data models eventually form a “spaghetti” architecture of disparate systems, which can lead to massive headaches in every business unit or department.  Cutting corners on Data Architecture in the short term can have long term ramifications If you have any questions, need clarification, or want to talk data – reach out to us here!

Data Warehousing

As your business grows, it may become more difficult to make decisions that steer your business in the right direction, especially if all your data is housed in various places. We are often approached by businesses who have their data reporting in different systems that don’t talk to one another seamlessly.

What if you could unify all of your data into a single location and give precise business metrics for your business’s KPIs?

Enter Data Warehousing: the technology that pulls data from multiple sources so that it can be analyzed together for a better understanding of corporate performance(s). 

How Can Data Sleek Help Build Your Data Warehouse?

We work through five core pillars of data warehousing for the benefit of your business. These pillars are: Dimensional Modeling, Data Integration, Transformation, Data Governance, and Analytics. In using our team of data experts, we dig deeper into your data and find actionable insights that give you an advantage over your competition.

Our Five Pillars of Data Warehousing

Dimensional Modeling

This is the data structure technique optimized for data storage in data warehousing. This is important so that there is faster retrieval of your data. The business intelligence (BI) systems we build for you will combine the right facts and dimensions to fulfill all your report needs.

Data Integration

We love technology and place a high priority on keeping up with the latest trends in data management. This is why we specialize in online pipeline services like Fivetran and Stitch Data to stream data into the warehouse. Then, we connect to all of your data sources (like Facebook, Google Ads, ZenDesk, Segment, Braze, Web-App logs, S3, APIs, etc) that are used by both your employees and customers.

Transformation

We use DBT (Data Building Tool) to transform your data into analytics. DBT is a development environment built for data analysts and engineers to transform data through select statements. We use DBT to write code that allows your reports to run dramatically faster! Your fears of data loss or being held captive by your data is a thought of the past with the help of Data Sleek.

Data Governance

Data Governance is defined as a set of principles and practices that ensure quality through the entire lifecycle of data. It is a practical and actionable framework to identify and meet information needs. Part of managing data are the system rules,  processes, and procedures, to make sure there is consistency and accountability for information processes and their execution and usage. At Data Sleek we can help you implement a data governance solution to keep your data safe, clean, compliant, and to provide a single source of truth for your data warehouse.

Analytics

Dimensional modeling and analytics are closely tied together. Proper modeling fact and dimension tables are key to report efficiency, allowing high user concurrency while providing fast reports and flexibility. Fact and Dimension tables give the ability to generate aggregated tables which can summarize your data any way you want while providing fast report response for data visualization tools such as Tableau, Mode, Qlik ,or Looker.

Leveraging the skill sets of our team at Data Sleek, you are no longer worrying about the additional man hours it could take to pull all of your data from its independent sources. Instead, you are building toward logical and successful business decisions. We use popular methods like Snowflake Computing for warehousing and SingleStore (formally MemSQL) for fast data ingestion with real-time analytics.

Data Sleek helps your data maintain its integrity from the point of sale up to the board of directors, allowing you to rest easy at night knowing everything is taken care of.

Data Lake and AWS Lake Formation?

Data Lakes, like warehouses store data. But they differ in the type of data stored – data lakes
are vast pools of raw, unprocessed data, the purpose of which is not yet defined. Data
Warehouses are processed, structured ,and filtered data, stored for a purpose.

Data Sleek supports Data Lake Formation when applicable for future use based on business
need.

Why Should You Choose Data Sleek?

If you are a small or medium-sized business who wants to go from “just getting by” with your analytics to seeing actionable insights in a snap, we can help. In choosing Data Sleek, your data goes from multiple locations (that are potentially unreliable) to a system that is secure and provides your business a single source of truth. Whether you need large batches of data with quick turnaround or expansive reports with simple queries, we can help you.

Data Sleek can build custom business intelligence (BI) dashboards that you can use for decision-making, problem-solving, and discover patterns hidden in your data. This allows you to navigate your business’s market and come out the winner above your competition every time.

Let us handle your data so that you can do what you do best. Contact Us today for more information.

SnowFlake Computing : The Best Data Warehouse Solution?

In the last few years, the term “snowflake computing” has gained momentum in the data warehousing world.

This is due to a growing “DWaaS,” otherwise known as “Data Warehouse as a Service” company called Snowflake Inc., which was founded in 2012.

As the need for data management grows, businesses must remain agile in how they store and analyze their data.

Today we will attempt to answer the question, “Is Snowflake computing the best data warehousing solution?”

In A Hurry?

  • Snowflake computing is a “data warehouse as a service” (DWaaS) solution from Snowflake Inc.
  • It centralizes your data into a cloud-based solution that streamlines your BI and reporting analysis.
  • Snowflake Computing is a cost-effective warehouse solution because you only pay for what you use and can be scaled up quickly.
  • This data warehouse can easily share data with 3rd party accounts.

What is Snowflake Computing?

Snowflake is a data solution available in AWS (Amazon Web Services), Microsoft Azure, and Google Cloud.

The main objective of using Snowflake is to be able to scale, fulfill the majority of data analysis while drastically minimizing workload and maintenance of data storage.

Because Snowflake is a cloud-based service, there is no installation, configuration, or software or hardware management.

Although many solutions can store and process massive data loads, several factors make Snowflake unique in this category.

What Are The Benefits of Snowflake Computing?

There are many benefits to using Snowflake computing that has made it so popular since its inception in 2012.

Maintenance Requirements

First and foremost, Snowflake does not require any maintenance.

Many DBAs (Database Administrators) will tell you that a large part of their work is routine maintenance to ensure their data remains accurate and trustworthy.

Disk Requirements

Common problems DBAs face are lack of data on their disk drives.

Other issues arise from not having enough computing power dedicated to vast amounts of data transfer.

Snowflake eliminates these concerns by the nature of its cloud-based approach.

Now instead of having full-time personnel working on mundane tasks, they can be assigned more database modeling, architecture, and optimization tasks.

Personnel Assignments

With Snowflake, your database team can focus on providing data insights for the end-user in the business.

Snowflake improves your focus on the business and saves the business money related to maintenance.

When your data is centralized into one location, you can transform the data into actionable business decisions.

Scaling

First, Snowflake also helps separate computing from storage that provides the ability for instant scaling.

Secondly, once a business can scale computing units on the fly using SQL, there is more efficiency and less redundancy.

Thirdly, and most importantly, when you script your data transformation, you can use a line of code to resize your computing units.

This “instant scalability” is possible without the need to stop current workloads or wait while data clusters are load balanced.

Besides the increase in efficiency, cost-saving is massive compared to traditional on-premise solutions.

Modernization

Snowflake brings your data warehouse operations into a modern world.

When your data is centralized efficiently, it can be utilized by all of your users and applications seamlessly.

Data Science

Snowflake simplifies and accelerates your MI (machine learning) and AI (artificial intelligence) initiatives with high-performance data.

The increase in computing power relative to traditional DWH solutions enables instant and infinite possibilities.

What Are Common Problems That Snowflake Computing Helps Solve?

There are many benefits to using Snowflake as your data warehousing solution.

Let’s dissect the top reasons why you should consider Snowflake computing.

Centralization – Single Source Of Truth

Firstly, Snowflake computing allows businesses to consolidate their data into one centralized location.

As the number of data sources increases over time, a common problem of “spaghetti architecture” arises, causing massive bottlenecks.

When data is disseminated into many locations, it gets more challenging to manage quickly and efficiently.

Often data is lost, or worse, reported inaccurately.

By using Snowflake to consolidate data pipelines, using FiveTran and DBT for example, a business can now efficiently analyze the data and make close to real-time business decisions.

When done effectively, it can have profound effects on bottom-line revenue.

Data Warehouse @ Scale

Secondly, as demand for the consumption layer grows, businesses are faced with scalability issues.

Applications, dashboards, and queries start to run slow, and engineering teams struggle to optimize under Amazon RDS or other warehouse solutions.

With Snowflake, it is not uncommon to see applications processing speeds increase by 2-3 times compared to previous solutions.

This increase in speed also allows Business Intelligence analysts to derive new insights from their data quickly.

Engineering teams can also benefit from the ability to support their testing and development environments more quickly and easily.

Cost –  Pay For What You Use

Thirdly, and most importantly, many businesses emphasize cost as the primary reason for choosing their Snowflake warehousing solution.

The two layers in Snowflake computing – storage and computing – can be paid for separately. Furthermore, you only pay for queries executed against warehouse unit. The smaller the warehouse unit, the cheaper the cost. At anytime, you can switch to a larger warehouse unit using SQL for just one query, then scale back to the original warehouse unit.

Snowflake offers a pay-as-you-go pricing model and can scale up or down depending on your needs.

Other pricing models require an hourly rate regardless of actual computing resources used.

Meaningful Insights

In conclusion, because Snowflake improves efficiency and cost, more time and money can be spent on data analytics.

Better data analytics leads to better front-end dashboards for senior management to quickly analyze trends in their business.

How To Scale Your Business with Snowflake Computing

Snowflake computing allows businesses to distinguish between storage and computing options.

The result gives your business a clear advantage of on-demand scaling.

You can now scale resources automatically and without harming your data accuracy.

Most traditional data warehousing solutions take days or weeks to scale.

Because Snowflake allows for a centralized “single source of truth,” your data-driven dashboards can seek new revenue growth opportunities.

With Snowflake, business activities that usually required weeks or months of hardware implementations can now occur near-instantly by spinning up new data clusters.

Top Data Warehouse Alternatives to Snowflake

Below are the most common Snowflake computing competitors in the DWaaS (Data Warehouse as a Service) space.

Amazon Redshift

Snowflake and Amazon Redshift are very similar implementations of clustered data warehouses.

Snowflake is generally a bit more expensive to run than Redshift but is dependent on the underlying technology and business model used.

If you can dynamically compute your data clusters over time and keep tight controls on adding additional clusters, the costs between SF and Redshift are virtually the same.

Google Cloud

Google’s Cloud DataProc is generally regarded as the best managed Hadoop framework available on the market.

It is known for its speed when scaling up nodes on local SSDs and has often been clocked up to 100 times faster than other solutions.

Microsoft Azure

Microsoft Azure is a well-known data warehouse solution due to its parent company, Microsoft, which is prevalent in the computing world.

Snowflake and MS Azure use different SQL versions, and it is commonly said that Azure’s version (SQL DW) has too many limitations.

Many say that Snowflake has a much more solid pricing structure than Azure and, therefore, a better DWH solution for most businesses doing BI.

Snowflake Computing Use Cases

There are specific use cases for migrating to Snowflake that many businesses will benefit from using.

Data Sharing

Many businesses have a requirement to share data with 3rd party accounts.

Using Snowflake computing can be done securely without needing to leave a copy of that data on centralized servers.

XML and JSON Support

If your data warehouse deals with many semi-structured data sources like XML or JSON, Snowflake will provide better support than other solutions.

BI and Reporting Workloads

Snowflake is an excellent choice for performance-based BI reporting and analytical workloads.

These workloads usually take just a second or more to run when on a Snowflake-based warehouse model.

Snowflake Computing Conclusion

As you can see, Snowflake computing offers many compelling reasons for being your go-to data warehousing solution.

The speed and efficiency it offers far outpaces its competitors from larger, well-known industry giants like Microsoft and Google.

Snowflake is now valued at around $13b, and they are rapidly growing their share of the marketplace.

If you are seriously considering moving to a Snowflake data warehouse, we would love to speak with you.

At Data Sleek, we specialize in Snowflake computing and can apply our expertise in this field to your data warehouse migration and implementation.

We have years of experience with small and medium-sized business customers.

Let Data Sleek be your go-to Snowflake Computing experts.

If you are interested in learning more about how we use Snowflake computing, please navigate to our Contact Us page or fill out our questionnaire.

What Is Spaghetti Architecture and How To Avoid It?

Modern businesses need to store and analyze vast amounts of data to compete in their respective marketplaces.

As new tech services promise faster and more efficient ways of extracting critical insights about business, many legacy businesses struggle to merge old technologies with new ones.

This merging process almost always leads to the term “spaghetti architecture.”

In today’s post, we will discuss spaghetti architecture in more depth and give you a few ways to avoid or minimize its effects on your business.

In A Hurry?

  • Spaghetti architecture happens on the application and data layers.
  • Spaghetti architecture can lead to duplicate processes, high costs, and lower company culture.
  • Choosing the right technologies the first time can decrease the chances of spaghetti architecture.
  • Spaghetti architecture must be solved if a business wants to scale.

What is “Spaghetti Architecture”?

The term “spaghetti architecture” can be defined as an Information Technology (IT) problem that hinders a business’s ability to rapidly decode and transform their applications and data to meet ever-changing requirements.

Spaghetti architecture is a metaphor derived from the appearance of a plate of spaghetti.

The spaghetti noodles represent each business tool that is tangled into infinite strands of complexity.

These are the most common areas of an organization’s technical infrastructure that fall into the spaghetti conundrum:

Application Spaghetti

Businesses add more and more applications to their infrastructure for tracking sales, customers, and other relevant data.

Each application has its way of communicating with each other, some using APIs while others remain siloed with little ability to integrate into the greater whole.

Some applications are in use by specific departments without the foresight of how they will integrate with other applications as the business grows.

Sometimes applications come from mergers or acquisitions and cannot be easily integrated or discontinued without massive impact in the business.

The net result is a complicated, inefficient, and sometimes expensive management of these applications.

The complexities cause undue stress on IT personnel who must ensure the applications are secure and maintain business objectives.

Data Spaghetti

Below the application layer of your IT infrastructure lies the data layer.

The data collected by these applications need a silo or warehouse that will house and analyze the data.

When applications are not natively or seamlessly integrated, the data often cannot be merged to extract meaningful insights.

The ensuing disconnection leads to poor data management, wasted customer growth opportunities, and gaps in security.

Regulations like GDPR (General Data Protection Regulation) force businesses to adapt more limits on the amount of data stored.

Data Sprawl

Data sprawl is similar to data spaghetti but adds the additional headache of leaving silos of data separated from the central data warehouse.

In these cases, the data silos grow in size yet do not provide any value to the business because their data points cannot be centralized.

Data sprawl also represents a cultural problem that can negatively affect a company’s revenue.

When departments are all using different applications, data sharing leads to biases between managers or department heads.

This causes internal conflict and distrust within the organization.

Problems Caused by Spaghetti Architecture

The problems caused by spaghetti architecture can dramatically affect the bottom line revenue of any business.

When a business delays or hesitates when solving the underlying issues, the problems build up over time and often cost more to fix later down the road.

Here is a brief list of common problems stemming from spaghetti architecture:

Customer Data

Customers don’t care about how a business operates internally; their main concern is getting the right product or service that solves their problems.

When a business struggles to match the right product at the right time to the right customer, they fail themselves and the customer.

If a business struggles with spaghetti architecture, they will fail to meet the needs of new or existing customers.

They will fail to understand their customers and therefore be at a disadvantage in their marketplace.

Chaotic Systems

Multiple systems create chaos when used inefficiently.

With so many data points and data silos, various departments will struggle to harmonize and be in sync.

Duplicate systems and processes become unscalable, and the result is inaccurate data and exposure to risk.

A “one size fits all” data approach is a mythical creature like a unicorn.

By acknowledging this, you put yourself in a position to make informed decisions based on data and industry best practices.

Unproductive Personnel

Data fragmentation caused by spaghetti architecture can kill efficiency in other areas of your business.

For example, when your support team cannot access the right customer data, it may fail to solve the customers’ problems and may lose that customer.

When tasks are duplicated, it leads to poor employee morale, which leads to strained company culture.

Maintenance Costs

A sophisticated IT architecture means increased maintenance costs, whether it is cloud-based or on-premise.

As your IT department grows, and new data integration challenges are faced, your data’s consistency is at stake.

When you add complex data synchronization, data mapping, and real-time interfaces, these small problems become big problems.

Maintaining a broken system will lead to impaired judgment, and the “sunken cost fallacy” that will cloud your ability to remain agile.

How To Avoid Spaghetti Architecture

The symptoms of spaghetti architecture can be cured or altogether avoided if proper planning is involved.

While not all symptoms have cut and dry solutions, taking these 6 points into account will dramatically decrease your chances of developing a chaotic environment in your IT department and save you millions of dollars down the road.

Reformulate vision

Sometimes you must go “back to the drawing board” and restructure your approach to business.

Modern businesses must continuously innovate both organizationally and technologically.

The business that remains most adaptive to change will beat their more docile and stagnant competitors.

Analyze data before applications

Start the evaluation of your IT processes at the data layer.

Try to find areas that are duplicated, inefficient, and unnecessary.

It would help if you also audited your data processes for security risks and obsolete technologies so that your business stays up-to-date and in compliance.

Simplicity

The challenge of running a complex business is to make each process as simple as possible.

It is easy to create complexity in your business, which almost always leads to spaghetti symptoms.

By putting a premium on simplicity, you build value back into your business and make it easier to move and pivot down the road.

Choose the right technologies.

Choosing the right applications and processes the first time helps you to avoid the need for restructuring down the road.

A great way to know if the technology you are choosing is to evaluate 2-3 vendors and do a small “Proof of Concept.”

A Proof of Concept is when a small project is completed at a minimal cost.

A POC allows you to see what the technology can do for your business at scale and will hedge your investment in that solution.

The time and cost you invest in the evaluation process should be a drop in the bucket to the massive savings and profit you will realize when choosing the right technology.

Measure and adjust

A common saying in engineering goes “what gets measured gets improved” and is a great philosophy to avoid the problems associated with spaghetti architecture.

As the adage goes, “measure twice, cut once.”

Taking time to measure your internal IT processes and adjusting them based on these parameters will have a profound effect on your bottom line revenue.

Patience

As with most business processes, being patient and allowing things to develop over time cannot be understated.

While it is essential to have a sense of urgency in your business, allowing things to develop organically over time is the best way to avoid spaghetti architecture.

When combined with the points above, patience will allow you to make well-informed decisions in your business.

Spaghetti Architecture Conclusion

Today we have outlined many reasons why you want to avoid spaghetti architecture.

While the symptoms of spaghetti architecture can remain contained, the long-term effects can prevent your business from scaling.

At Data Sleek, we can help you diagnose your spaghetti architecture symptoms and provide an accurate diagnosis with the most current data architecture solutions.

Data Sleek is comprised of expert data engineers and business analysts who can recommend various applications and database solutions that “untangle” your IT processes.

We specialize in data warehousing, data engineering, data science, and data visualization.

When your business is free to scale, the revenue potential is realized.

If you are dealing with any of these spaghetti architecture symptoms, we would love to talk to you.

Please go to our “Contact Us” page and leave your contact information.

We have helped businesses overcome the challenges of spaghetti architecture for the last five years. We look forward to learning more about your business challenges.

How To Choose A Data Solutions Agency?

Choosing a data solutions agency is a challenging decision for your business.

With so many technologies that can connect your data sources to internal platforms, your due diligence in choosing a data solutions partner can be a long and arduous process.

Today we discuss how to choose a data solutions agency and what factors to look for to give your data projects the best possible chance for success.

In A Hurry?

  • A Data Solutions Agency provides data solutions for the architecture, engineering, warehousing, and visualization of data.
  • There are many standards and compliance factors to meet when working with customer data.
  • Before a project is launched, most agencies will provide a Statement of Work that outlines each stage of work in the project.
  • A Proof of Concept is a smaller project that proves the knowledge, ability, and communication of a data solutions agency.

What Is A Data Solutions Agency?

A data solutions agency is a business that provides data architecture, warehousing, engineering, and visualization.

They may also provide data integration and can build front-end dashboards for data visualization.

Data solutions and consulting can range from:

  • DaaS (Data as a Service)
  • Data engineering
  • Data architecture
  • Database management
  • Database optimization
  • Data pipeline integrations
  • Front-end design
  • Back-end development
  • QA (quality assurance)

The core function of a Data Solutions Agency is their work with data architecture and engineering.

Many start-ups tend to use a single database technology and as they grow, they run into scalability issues.

A Data Solutions Agency will help the business decouple these “tangled” services which free up application bandwidth and prevent future bottlenecks.

Your data sources can range from Facebook or Google ads, email autoresponders like Mailchimp, transaction data from POS (point of sale) kiosks, or support tools like ZenDesk.

Management and integration of these systems into a centralized platform is usually best left to experts rather than in-house or homegrown solutions.

How To Choose A Data Solutions Agency

Below we will outline the most important factors to look at when choosing the right data solutions agency.

Timing

Understanding your critical business needs is the first step in choosing the right Data Solutions Agency.

There are several factors to consider in regards to timing that you should keep in mind:

  • The timeframe to implement these new data solutions?
  • When is the right time to implement a new data solution into your existing business processes?
  • How fast can the data agency integrate your data sources or formulate a plan for new sources?
  • Will your business suffer any downtime while your data sources are integrated with new solutions?
  • Will migrating from your existing solution to the new solution require the loss of revenue in the short-term?

These are the critical questions you must answer before selecting the right Data Solutions Agency.

Most agencies will be familiar with the migration and implementation process and should be able to answer these questions after they have scoped your project.

Standards

When searching for a data solutions agency, it is vital to understand the underlying standards that relate to your data, and specifically your customer data.

Standards like SOC I and II deal specifically with businesses that store customer data like names, email addresses, phone numbers, and credit card information.

If you are a business operating in the EU (European Union) or have customers that are in the EU, you may fall under the GDPR standard.

GDPR is short for General Data Protection Regulation and related to the storage of personal information of customer data.

If your business falls under the GDPR requirements, you will need a data solutions provider that understands the complexities of GDPR so that you are not in breach of this mandate.

Failure to account for such standards could mean big trouble down the road in terms of fines and legal risk.

Technology Roadmap

In the fast-paced world of technology, a solution that provided adequate results three years ago may soon become antiquated and no longer suitable for your business.

A competent data solutions agency will understand the rapid pace at which technology and regulations change and provide a “roadmap” in anticipation of future changes.

The agency should stay up-to-date on the latest data trends and provide you with a thorough roadmap to help you navigate data management changes for the foreseeable future.

Failing to plan for the future means your business may lose revenue, be locked into disparate technologies, or cost you more money down the road.

Make sure the data solution provider you choose has a plan to account for these changes that are inevitable to occur.

Data Security

Keeping data secure is a crucial component of choosing a data solutions agency.

Most businesses that collect customer data to analyze trends and make business decisions have a data classification scheme that defines the data collected and where that data resides.

The ability to protect the data when it is in the data pipeline and stored for archival purposes is something that the agency will help solidify.

Any data loss or breach of data could have profound negative consequences on your business.

Even small and medium-sized businesses must account for the data that they store.

A data solutions agency will understand how data is collected and stored while providing the best recommendations for data security operations and procedures.

Contracts

Data solutions contracts can be complicated and overwhelming.

Many technical components must be addressed when hiring a data solutions agency.

The ability to articulate each section of the agreement without overcomplicating things is a virtue of a trustworthy data solutions provider.

Data solutions projects typically begin with a scoping document called a Statement of Work.

The Statement of Work will outline every detail of the type of work that the agency will do.

It will drill down and specify what is needed from your business to complete each step.

Some engagements begin with a POC (Proof of Concept) in which a smaller project is done first to prove the quality of work that the agency will do.

Upon successful completion of the POC, the business will hire the agency for a much larger project.

The POC is a way to show the abilities of the agency without overcommitting to a massive project.

It is an excellent way for the agency to prove it’s knowledge of data management and give the business an idea of how they will communicate and meet deadlines on a more extensive engagement.

Reliability and Accuracy

Having a reliable data solutions partner is a vital component of your decision.

The data solutions agency you choose should be reliable and should also earn your trust through honest and transparent communication.

When evaluating a potential partner, it is common to speak to their previous customers to get an idea of the type of work they do.

  • How do they communicate?
  • Do they deliver on what they promise?
  • Were there any issues due to incompetency or lack of knowledge in a given field?
  • Will they provide accurate results?

It is crucial for a potential data solutions agency to answer these questions before embarking on any paid project.

Data Migration

Data migration is quite common when integrating your data sources into a single business dashboard.

Some businesses rely on disparate or outdated data models that are losing them revenue opportunities.

The data solutions agency you choose should be proficient at data migration and provide adequate proof that their proposed solutions will work.

Business Health and Company Profile

When evaluating a data solutions partner, you will want to evaluate such factors as:

How good is the business from a revenue perspective?

How long have they been doing engagements similar to yours?

What happens when engagement goes sideways?

Who are the business principles, and what are their track records?

Other items to look at are case studies, success stories, customer on-boarding, and customer success management.

Vendor Relationships

The Data Solutions Agency you choose should have strong relationships with the technologies they recommend and work with.

They should be up to date with their preferred database partners’ products and/or services while maintaining objectivity and honesty to you, the end customer.

The best Agencies will form partnerships with key vendors and commit themselves to learn and master their technologies.

Compliance

Like certifications and standards, you will want to evaluate how the potential data solutions agency will keep you and your data in compliance with local, state, and national laws.

Failing to keep you in compliance with these levels of laws could mean fines and possible legal actions for your business.

How will the agency keep you in compliance?

What measures does the agency take to stay up to date on data compliance best practices?

If you fall out of compliance, what will the agency do to remedy the situation so that you are back in accordance?

These are the types of questions you want to ask during your due diligence in choosing the best data solutions agency for your project.

Cost

Cost is a huge factor when choosing the best data solutions agency to work with and it should not be the sole factor, as the adage “you get what you pay for” holds especially true for data solutions engagement.

The most expensive quote is not always the best option, just as a low-cost solution is not the worst solution.

When taking all factors into account, your project’s cost should ensure that the ROI (return on investment) is clearly articulated and delivered upon.

Best Data Solutions Agency Conclusion

There are many factors to consider when choosing the best data solutions agency for your business.

Each consideration is a piece of a puzzle that forms your overall success plan.

At Data Sleek, we take great care to answer all of your questions during the evaluation process.

We have worked on many engagements in various industries and have developed a process that includes a custom Statement of Work for your project.

We have previous customers that would love to share their success with you.

Our customer philosophy is when you win, we win.

We will take great care to communicate our work, and it’s related benefits to your bottom line revenue goals.

We look forward to talking more about your project and the types of solutions we can provide.

If you have a data solution project in mind, feel free to navigate to our “Contact Us” to tell us more about what you need.

How To Simplify Data Pipelines with FiveTran?

With the massive and continuing growth of the global datasphere and cloud-based applications and activities, businesses have become more and more dependent on data. Organizations that can turn massive amounts of data into actionable insights and bleeding-edge products will thrive, while others will falter.

Today we will discuss Fivetran, a tool that allows a business to radically simplify its data pipelines.

Fivetran:

  • Landed its first customer in 2015 and now serves over 1,100 customers.
  • Is based in Oakland, CA, and currently valued at $1.2bn.
  • Is often paired with Snowflake data warehouse, and can also send data to Redshift, BigQuery, and Azure and other destinations.
  • Can connect to 100+ different data sources and stream data to a data warehouse of your choice.

What is Fivetran?

Fivetran offers fully-automated data connectors that replicate data from sources such as:

  • Enterprise software tools (i.e. SaaS)
  • Operational systems and transactional databases
  • Event tracking from web browsers and applications
  • File storage
  • Sensor data, i.e. internet-of-things (IoT)

to destinations such as data warehouses and data lakes.

Data connectors by Fivetran are zero-maintenance and automatically keep up with API schema changes, so that users don’t need to worry about data pipeline maintenance downtime. In short, Fivetran automates the most tedious and onerous tasks within data engineering, allowing data and IT teams to focus on producing reports, dashboards, predictive models, and machine learning applications.

A recent data analyst survey by Fivetran found that only 34% of data analyst time is wasted trying to access data, and only 50% of data analyst time is spent analyzing data. Another study found that of the Fortune 500, around 85% are unable to fully leverage their data for a competitive advantage. These findings point to a vast, unmet need for fast, efficient data integration.

Data pipeline services like Fivetran save users the costs, time, and hassle of creating and maintaining data pipelines.

Fivetran is Part of the Modern Data Stack

Cloud-based data sources, especially SaaS applications, have exploded in popularity. The challenge of integrating this huge variety of data has been met by the development of cloud-based data integration tools. Fivetran is one element of a suite of cloud-based technologies that facilitate data integration. In total, the parts of this modern data stack include:

  1. Data pipelines like Fivetran
  2. Cloud-based warehouses like Amazon Redshift, Google BigQuery, and Snowflake
  3. Data transformation and data modeling tools such as dbt
  4. Fast, browser-based business intelligence tools with easy-to-use interfaces and collaborative features, such as Looker, Tableau, Qlike or Mode

For more advanced use cases, such as those involving unstructured data, data lakes may be used as destinations, and data science platforms may be layered on top of transformations.

Simplify with Data Sleek, Fivetran, and Snowflake

With the growth of new data sources, technologies, and tools, the ability to move data rapidly and efficiently has become a basic business need. Your internal data is a powerful asset. To stay ahead of the curve, consider deprecating classic data pipelines in favor of cloud-based, modern data pipelines.

This move is made more accessible with Data Solutions Agencies like Data Sleek. With Data Sleek, you get a team of data engineers and scientists with experience in Fivetran, dbt and Snowflake computing. Let Data Sleek evaluate your data bottlenecks and lost revenue gaps and help you close them.

Building a long-term business requires sustainable data infrastructure and the expertise to manage it. Many of our customers utilize our skill-sets while maintaining in-house staff as we complement what you are already doing. This helps your Team develop the relevant skills in parallel with our experts. We will work together to help you define technical specifications following best practices.

If you are considering a rapid data pipeline using Fivetran, please reach out to us. Or, if you’re ready to talk, you can simply navigate our “Contact Us” page and tell us a little more about you and your business.

We leverage technologies like Fivetran to help our clients, and we can do the same for you.

Top 10 SaaS KPIs for Growing Subscription Businesses

The success of any subscription-based SaaS business depends on the data they collect and analyze.

Pulling meaningful insights from this data is a challenge that all SaaS businesses face.

Today we will discuss the underlying reasons why SaaS businesses track KPIs and the most important KPIs to track.

In A Hurry?

  • KPIs are Key Performance Indicators.
  • KPIs are usually tracked by the IT or Business Analytics team and presented to the executives and board directors.
  • SaaS businesses sell subscriptions to their services most often on a monthly or annual basis.
  • Most one-time fees for setup upsells, or discounts are not counted in monthly KPIs.

What Are KPIs?

In modern technology vernacular, the term “KPI” or “KPIs” stands for Key Performance Indicators.

These are statistics that a business can measure to estimate the stability of their business.

In regards to the SaaS (Software as a Service) model, these KPIs become more critical due to their subscription business model.

Regardless of the services you provide, the trick is identifying the smaller set of metrics that help determine your business’s health.

Why Are KPIs Important for SaaS Businesses?

Subscription KPIs are vital for tracking the success of your business.

Considerable time and effort must be taken to track and record the following KPIs to maximize revenue and avoid costly business decisions.

Poor performance in one or more KPIs will give you the necessary data to identify and prevent negative trends in your business.

Tracking SaaS KPIs also allows you to plan future revenue so you can grow more comfortably.

The struggle most SaaS businesses face is accurately reporting KPIs and integrating various data sources into one centralized location.

This is where Data Solutions Agencies like Data Sleek come into play.

By utilizing the skill sets and experience of 3rd party agencies, SaaS companies can avoid personnel costs while effectively leveraging their in-house staff.

Most SaaS startups do not have full-time database administrators or data analysts which makes it difficult to leverage accurate and real-time KPIs.

By relying on 3rd party agencies, they can still transform their data pipelines into meaningful business insights.

The health of any SaaS business comes down to the ability to acquire and retain active subscribers.

Businesses that fail to maintain accurate and timely records of their customers can fall into the trap of overpaying for customer acquisition.

Customer growth is paramount for both investors and key executives.

Below we will outline the Top 10 SaaS KPIs that every subscription-based SaaS business should be tracking.

Top 10 SaaS KPIs for Growth

All subscription-based SaaS businesses should track the following Key Performance Indicators (KPIs).

Failing to do so could mean costly expenses or loss in revenue or customer base.

Accurately tracking and analyzing the following KPIs should be an integral part of your IT and business analytics team’s day-to-day duties.

  • Active Subscriber Count (ASC)

Active Subscriber Count is one of the most obvious metrics to track for SaaS.

In short, it is the number of paying customers for your service at any given time.

Most SaaS businesses sell their services on a monthly or annual subscription model.

Most SaaS businesses allow customers to sign up for their service at will and can cancel at any time or once their contract has expired.

Because the active subscriber count is continuously changing, key decision-makers must have easy access to the most up-to-date data.

Active Subscriber Count can also be broken down into a few sub-categories like:

  • Which subscribers are the most profitable?
  • Which are the most engaged with the product (who uses your product the most)?
  • Which customers are most likely to stay customers.
  • Which customers are most likely to churn (also known as canceling their subscription to your service)?

ASC is one of the best KPIs to use in executive committees and board meetings because it tells the “story” of business growth.

  • Customer Acquisition Cost (CAC)

Customer Acquisition Cost (CAC) is the total sum of marketing and sales efforts to acquire a single customer.

CAC is another critical KPI to track because a high cost to get a customer can be harmful to overall business health.

When combined with other KPIs like Active Subscriber Count (ASC) or MRRC (Monthly Recurring Revenue Churn), you begin to paint an excellent outlook for your business.

A standard formula used to calculate CAC is:

CAC = Total Sales and Marketing Costs / Number of Customers Acquired 

When SaaS business begins, its CAC can be exceptionally high as it gains its first few hundred customers.

It is not uncommon for CAC to be 150-200% or more in their first year of business.

If done correctly, businesses with high CACs can compensate for the initial loss of revenue by upselling their current customers to more expensive services or multi-year commitments.

Once a subscription-based SaaS company establishes their credibility in the marketplace, they can see a CAC of about 20-30%.

CAC can also provide insights into the effectiveness of your marketing and sales efforts.

Many SaaS businesses struggle with centralizing all marketing channels and attributing each acquisition of new costumes accurately.

Finally, tracking CAC accurately can provide future insights on the ability to scale and remain profitable.

  • Customer Lifetime Value (CLV)

Customer Lifetime Value is the revenue received by each customer over the lifetime of their subscription.

It is also the prediction of revenue a business will receive over a defined period.

Like ASC, you can add segments of CLV for further insights into your profitability.

Other factors like frequency, recency, and monetary value should not be ignored.

Simply put, CLV is the most critical KPI for driving actionable insights.

Increasing CLV should be the focus of your marketing and sales strategy.

CLV can also tell you where to invest more resources for acquiring the best customers and shy away from the least profitable sales channels.

  • Monthly Recurring Revenue (MRR)

Monthly Recurring Revenue is the total of all revenue from recurring subscription service plans minus all of the one-time or non-recurring payments.

MRR is the backbone of all SaaS KPIs and provides insights into plan upgrades and downgrades, pricing strategies, and discounts.

Other subcategories of MRR include:

New MRR – converted paid customers in a given timeframe.

Expansion MRR – the increase in MRR from existing customers over a given time.

Contraction MRR – the decrease in MRR from existing customers over a given time. 

Net New MRR – the delta of current MRR to Expansion MRR over a given time.

Monthly Recurring Revenue and its sub-categories are essential for each business to understand and utilize.

Without strict adherence to these KPIs, a business can quickly lose money and customers.

  • Monthly Recurring Revenue Churn (MRRC)

Just as MRR tracks monthly subscription revenue, MRRC or Monthly Recurring Revenue Churn measures how much your monthly subscription revenue was lost.

When a customer leaves your company and no longer pays for services, they are considered “churn.”

MRRC is measured by the number of customers who cancel or do not renew their subscriptions in a given month.

  • Average Revenue Per User (ARPU)

ARPU (Average Revenue Per User) is a critical SaaS KPI to track.

Tracking your ARPU KPIs allows you to see how much value your customer base is providing your business.

To calculate your ARPU, divide the MRR from your active customers by your total number of customers.

Tracking ARPU lets you make educated plans for current and long term business decisions.

ARPU also gives you insights into which customer personas or “avatars” are most profitable.

  • Customer Churn 

Just as ASC measured the number of customers at any given time, the Customer Churn relates to how many customers your subscription business loses at any given time.

Customer Churn can be beneficial in calculating with regards to particular marketing campaigns to measure how effective they were.

  • Months to Recover CAC

Months to Recover CAC or MRCAC helps determine the timeframe it takes to recover the CAC after you’ve closed a customer.

This KPI can help determine the effectiveness of marketing and sales campaigns and shed light on your customer onboarding processes and procedures.

The faster you can recover your CAC, the better off your long-term profitability will be.

  • Customer Engagement Score

The Customer Engagement Score is a SaaS KPI that measures how engaged your customers are with your service.

Customer Engagement includes the following factors:

  • How often do they log into your service?
  • What are they using your software for?
  • How much bandwidth do they use on your platform?
  • How many users they’ve set up with your service?

Customer Engagement is a crucial measurement that is a precursor to MRRC or customer churn.

If your customer is not interacting with your service or platform, they are less likely to renew at the end of their current subscription.

Implementing ways to increase engagement such as product training, assigning a Customer Success Manager, and periodic check-ins with your customers will minimize churn.

  •  SaaS Bookings

This KPI metric is the total revenue that customers have pledged to your business in a given time.

It pulls together all of your sales and marketing channels to provide the most transparent way of calculating revenue growth.

It is not necessary to include the following in your SaaS Bookings calculations:

  • Discounts
  • Setup Fees
  • One-time fees
  • Credit adjustments

We also encourage you to measure your proportion of new bookings (new customers) to upgrade bookings (Expansion MRR).

This measurement will allow you to allocate more sales attention to upselling existing customers.

SaaS KPIs For Growth Conclusion

As you can see, there are many KPIs and sub-KPIs that every SaaS business should track and measure.

There is an old saying:

 “What gets measured gets improved.”

If you are not currently tracking any or all of these SaaS KPIs in your SaaS business, it may be time for drastic changes in your data approach.

At Data Sleek, we help SaaS businesses streamline data into meaningful analytical insights.

We work with cutting-edge data technologies like Fivetran, Snowflake Computing, and DBT which are key to building an efficient and scalable KPI reporting solution.

The combination of these powerful tools helps eliminate roadblocks and blindspots in your business.

Data Sleek can help integrate your data pipelines and analytics in a short amount of time and with limited resources.

We provide your company with human resource “elasticity.”

This means you can quickly build a dedicated team with the right expertise to fine-tune your KPI dashboards.

We will also mentor your business analytics team at the same time so that nothing is lost in translation.

Once the project is complete you can quickly scale down personnel as needed.

We want to help you reduce your overall cost and increase the speed of delivery.

If you’re interested in discussing how we can streamline your SaaS KPIs into meaningful insights, please fill out the brief questionnaire on your Contact Us page.

We look forward to working with you and helping you master your SaaS KPIs for continued growth!

Automating AWS SageMaker Notebooks

Introduction

SageMaker provides multiple tools and functionalities to label, build, train and deploy machine learning models at a scale. One of the most popular ones is Notebooks Instances which are used to prepare and process data, write code to train models, deploy models to Amazon SageMaker hosting, and test or validate the models. I was recently working on a project which involved automating a SageMaker notebook.

There are multiple ways to deploy models in Sagemaker using Amazon Glue as described here and here. You can also deploy models using End Point API. What if you are not deploying the models, rather executing the script again and again? SageMaker does not have a direct way to automate this right now. Also, what if you want to shut down the notebook instance as soon as you are done executing the script? This will surely save you money given AWS charges on an-hourly basis for Notebook Instances.


How do we achieve this?

Additional AWS features and services being used

  • Lifecycle Configurations: A lifecycle configuration provides shell scripts that run only when you create the notebook instance or whenever you start one. They can be used to install packages or configure notebook instances.
  • AWS CloudWatch: Amazon CloudWatch is a monitoring and observability service. It can be used to detect anomalous behavior in your environments, set alarms, visualize logs and metrics side by side and take automated actions.
  • AWS Lambda: AWS Lambda lets you run code without provisioning or managing servers. You pay only for the compute time you consume — there is no charge when your code is not running.

Broad steps used to automate:

  • Use CloudWatch to trigger the execution which calls a lambda function
  • The lambda function starts the respective notebook instance.
  • As soon as the notebook instance starts, the Lifecycle configuration gets triggered.
  • The Lifecycle configuration executes the script and then shuts down the notebook instance.

Detailed Steps

Lambda Function

We utilize the lambda function to start a notebook instance. Let’s say the lambda function is called ‘test-lambda-function’. Make sure to choose an execution role that has permissions to access both lambda and SageMaker.

Here ‘test-notebook-instance’ is the name of the notebook instance we want to automate.

#Starting a notebook instance
import boto3
import logging

def lambda_handler(event, context):
    client = boto3.client('sagemaker')
    client.start_notebook_instance(NotebookInstanceName='test-notebook-instance')
    return 0

Cloudwatch

  • Go to Rules > Create rule.
  • Enter the frequency of refresh
  • Choose the lambda function name: ‘test-lambda-function’. This is the same function we created above.

Lifecycle Configuration

We will now create a lifecycle configuration for our ‘test-notebook-instance’. Let us call this lifecycle configuration as ‘test-lifecycle-configuration’.

The code:

set -e

ENVIRONMENT=python3
NOTEBOOK_FILE="/home/ec2-user/SageMaker/Test Notebook.ipynb"
AUTO_STOP_FILE="/home/ec2-user/SageMaker/auto-stop.py"

source /home/ec2-user/anaconda3/bin/activate "$ENVIRONMENT"

jupyter nbconvert "$NOTEBOOK_FILE" --ExecutePreprocessor.kernel_name=python3 --execute

source /home/ec2-user/anaconda3/bin/deactivate

# PARAMETERS
IDLE_TIME=60  # 1 minute

echo "Fetching the autostop script"
wget https://raw.githubusercontent.com/aws-samples/amazon-sagemaker-notebook-instance-lifecycle-config-samples/master/scripts/auto-stop-idle/autostop.py

echo "Starting the SageMaker autostop script in cron"
(crontab -l 2>/dev/null; echo "*/1 * * * * /usr/bin/python $PWD/autostop.py --time $IDLE_TIME --ignore-connections") | crontab -
  • Brief explanation of what the code does:
    1. Start a python environment
    2. Execute the jupyter notebook
    3. Download an AWS sample python script containing auto-stop functionality
    4. Wait 1 minute. Could be increased or lowered as per requirement.
    5. Create a cron job to execute the auto-stop python script
    After this, we connect the lifecycle configuration to our notebook.

I would love to connect on LinkedIn —https://www.linkedin.com/in/taufeeqrahmani/

The Power of Analytics using Singlestore

Artificial intelligence and machine learning are the future of business.

Drawing meaningful insights from AI and ML will make or break most businesses in this new “AI Revolution” and new tools will be needed to manage all of this data. AI and ML are useful to decision-makers in identifying new, previously unseen patterns in data. Along with interactive data exploration, it helps us ask new questions and posit new questions beyond the standard business KPIs for a given business process. In recent years, businesses are moving beyond using AI and ML purely on historical sets of data and are now looking for effective and more frictionless ways to operationalize AI and ML. That is, to continuously run ML models on both live, operational data and historical data.

Let’s take a closer look at one of the fastest emerging data management technologies called Singlestore (formerly MemSQL) which supports operationalizing AI/ML.

In A Hurry?

  • Singlestore is the world’s fastest cloud database
  • Singlestore is the best choice for operational analytics, machine learning, and AI
  • Data acceleration gives your business the best data architecture
  • Singlestore delivers these capabilities through a unique convergence of data storage which is called SingleStoreTM.

What is Singlestore?

Singlestore is a distributed, highly-scalable relational SQL database that can run anywhere and is commonly known for its speed and ability to scale. What may be lesser known is that it provides all of the capabilities and benefits of popular NoSQL databases but with the full power of ANSI SQL. This means that you can support key-value and document-style data types and data access patterns alongside your relational workloads all in a single distributed database. This reduces the number of specialized datastores needed for any use case or application as well as reduces latency. Singlestore has also incorporated other NoSQL datastore functionality such as inverse indexes for full-text search, time-series, and geospatial capabilities.

Multi Purpose Engine

As a Data Architect or application architect, you can use these capabilities on an individual basis to eliminate the need for a front-side caching tier with technologies such as Redis, or a search index with Elasticsearch, or combine them. For example, you can store raw JSON documents for a product catalog as you would in Couchbase or MongoDB in Singlestore but then execute complex low-latency analytic queries on Singlestore’s columnar store view of that data for maximum speed, efficiency and reduction in data duplication.

Most businesses choose Singlestore to utilize these benefits:

  • Built for maximum ingestion speed 
  • Built for scale with its distributed-native system architecture
  • Delivers simplicity by supporting a spectrum of workloads in a single database technology
  • Delivers consistent low-latency query responses  for fast-changing data for transactional and analytical workloads 
  • High concurrency of users/customers
  • Easy adoption with familiar SQL and as a drop-in replacement for MySQL supporting the MySQL wire-protocol

Many reviewers have commented that Singlestore accelerates and simplifies data infrastructure. It runs smoothly and efficiently for both transactional and analytical workloads. It is a highly durable SQL database to work with. Data Analysts and CTOs like Singlestore because it works with all of your data sources like:

  • Facebook and Google ads
  • Customer data
  • Point of Sale data
  • Email auto-responders
  • Social media data
  • Transaction data
  • Customer history

Many Fortune 50 companies choose to use Singlestore along with other popular technologies like Hadoop.

Singlestore is a SQL database that ingests data continuously to perform operational analytics.

You can ingest millions of events per second with ACID transactions.

You can analyze billions of rows of data in these formats:

  • relational SQL
  • JSON
  • geospatial
  • full-text search 

There are two main products that form Singlestore:

Singlestore Helios

The leading product from Singlestore is called Helios. It is a full-managed SaaS database available today in multiple regions on AWS, Microsoft Azure, and Google Cloud Platform. It is billed as the world’s fastest cloud database for operational analytics, machine learning, and AI (artificial intelligence).

Singlestore Software

Singlestore is the self-managed operational database built for  speed, scale, and simplicity. It is available with the full functionality as a free download to use forever, but with a capacity limitation. It will help you realize the full potential of your data. Today, many SaaS startups are building their cloud-first product on Singlestore as you can see from their community and highlighted in the Community Conversations series.

Next, let’s take a look at what type of businesses are best suited for maximizing their data analytics with Singlestore.

Who Should Use Singlestore?

Uber, Fiserv, Kellogg’s, and Comcast are just some of the customers that use Singlestore, according to their “Case Study” section of their website. But don’t let that scare you. These days it is vital for small and medium-sized businesses to start thinking about BI (Business Intelligence) and Analytics. You can find many examples of these customers on the  Singlestore YouTube Channel, in the Singlestore Community, and in the Singlestore Forum.

Making a decision based on testing and data is more critical than ever.

Businesses that utilize data analytics in their business are more likely to succeed after five years.

Customer data and profiling have become one of the most popular ways to scale a business.

This model is based on 3 fundamental principles:

  • Customer Acquisition (Cost per acquisition)
  • Customer Repeat Business (MRR – monthly recurring revenue)
  • Increase in previous customer revenue (Month over month)

The types of businesses that could use Singlestore are:

  • Cloud services
  • e-Commerce
  • Logistics
  • Retail
  • Software
  • Artificial Intelligence
  • Time Series
  • Transportation
  • Social Media

Now, let’s take a look at some of Singlestores biggest competitors.

Main Singlestore Competitors

Singlestore is primarily designed for in-the-moment operational analytics use cases and cloud-native HTAP , but it can also handle OLTP and OLAP data warehouse scenarios. The competition for Singlestore is growing by the day and includes technologies like:

VoltDB

The most common direct competitor. Also a SQL-based in-memory relational database system but designed for OLTP.

Clickhouse 

Open-source OLAP database system. Designed for fast queries and data ingest. Complicated setup and many manual operations. Recognized by several data storage “engines” for maximum performance. Highly trained technicians are required.

Apache Ignite

In-memory data “grid,” which supports OLTP access. Uses SQL and other APIs. It also works well when using Apache Spark.  

MapD, Kinetica, SQream

GPU-powered databases with fast results on big data-sets. The right choice if you need the most rapid results with visualizations. Not suitable for OLTP.

Redshift, BigQuery, and Snowflake

Managed Data Warehouses for OLAP scenarios. Various levels of operational effort and personnel.

Cockroach DB

Distributed relational database with PostgreSQL. Focuses on high-availability, automatic sharding, and optimized replication.

CitusDB

An extension to PostgreSQL turns it into any distributed SQL database. Allows Postgres to be flexible and scalable across nodes. Does not use in-memory processing.

TimescaleDB

Another extension of PostgreSQL that creates tables with automatic partitioning. An excellent option for highly advanced analytics requirements.  

Microsoft SQL Server

In-memory OLTP to process tables with RAM. SQL is not natively distributed and only has replication and HA (high availability) setups available.

Apache Druid

OLAP system designed for low-latency and high-cardinality data. Pre-aggregating data as it comes in—very complicated setup.

Vertica, Teradata

Legacy column-store databases used by large enterprises. Very advanced features, mainly when used with Greenplum or MonetDB.

Splice Machine

Built on top of Hadoop and has tight integration with Apache Derby and Apache Spark.

Powerful Operational Analytics Using Singlestore

Most businesses are either starting new data architecture projects or are transitioning from legacy to modern architectures. The data analytics market is vast and valued at over $50b for the next 18 months.

Singlestore plays a crucial role in data scaling and customer profiling.

The most popular technologies that are used with Singlestore include:

  • Hadoop
  • AWS S3
  • Kafka
  • Spark
  • Tableau
  • Microstrategy
  • Looker

Next, let us look at the benefits of doing data acceleration with Singlestore.

What Is Data Acceleration?

Data acceleration helps organizations helps businesses address three challenges:

Movement – how to move data more quickly to where it is needed

Procession – how to gain actionable insights as soon as possible

Interactivity – faster queries submitted users and applications

Data acceleration allows companies to start treating data as a supply chain.

This enables the smooth flow and distribution of data to every ecosystem of partners, suppliers, and even customers.

Data acceleration allows a business to leverage more data sources and turn it into meaningful actions more quickly.

Data acceleration can give you three distinct advantages in business:

  1. Supports faster processing of crucial data points
  2. Supports faster connectivity
  3. Reduces user wait times

Once your business is ready to accelerate your data, you will need to focus on these architectural components:

Big data platforms

Complex event processing

appliances

Cache clusters

In-memory databases

Ingestion

Data services agencies like Data Sleek can help you choose the best technologies for your business, including Singlestore.

Next, we will learn more about why companies choose Singlestore.

Top 10 Reasons To Choose Singlestore

The main reasons businesses choose Singlestore are:

  1. To support in-the-moment, low-latency automated operational decisions and analytics to improve customer experience and business operations insights
  2. To support the rapid & cost-effective scaling of client concurrency for apps, users, and APIs 
  3. To simplify the data infrastructure environment and reduce as many as 11 technologies to 2 in some cases while continuing to support all of the data access patterns and styles, like key-value, document, search, relational, etc.
  4. To modernize to a distributed-native database and eliminate the costly maintenance of sharding middleware over single-node databases
  5. To provide cost-effective & affordable scalability
  6. To provide better customer experience through reliable high availability
  7. To provide a reliable system of record
  8. To run a modern database in any cloud or hybrid environment through support of Kubernetes
  9. To provide a data management solution that includes streaming data integration and change data capture as part of the product, not purchased separately
  10. And finally, to interact with a forward-looking innovative community which is driving the future of data management technologies

The benefits of modern data integration using Singlestore include:

  • Design once and can be used many times
  • Gain microscopic knowledge about the data
  • Manage complex environments
  • Optimized actions
  • Change, extend and migrate business data
  • Make quick business decisions

Below we have compiled the Top 10 business advantages to using Singlestore for your database architecture.

  • Application Integration

Use SOAP/REST APIs using cloud-based services.  

  • Huge volume data

IT departments are moving towards data lakes as the single source of truth and centralized data. Data integration tools make heavy use of Spark and Hadoop.

  • Data speed support

Data velocity is improved when using Singlestore. New data integrations should have the ability to handle data regardless of the size.

  • Event-based

Singlestore works with event-based frameworks. This allows a business to respond quickly to consumer and market trends.

  • Document-centric

With the increase in data regulations like GDPR, the tool you use should have compliance-related features that document data collection. This is a relatively new requirement for the latest data.

  • Hybrid integration

Most cloud-based data warehousing and engineering tools are cloud-based. Businesses that choose to remain on-premises must also be able to use cloud-based services.

  • Accessible through SOAP/REST APIs

Monitoring, securing, and organizing vast sums of data but be done using common frameworks like SOAP/REST APIs.  

  • Connectivity

New data integrations require connectivity to various data systems. When the data is analyzed and visualized, it becomes an essential tool of the business.

  • Elastic

Singlestore allows your data architecture to be elastic based on day to day changes in the business. If a data analyst leaves the company, it should not hinder the overall operation of the business.

  • Integration as a Service (IaaS)

Singlestore allows your business to be cloud-based and data-driven. As business data gets more complicated, Singlestore gives you the best data insights.

Singlestore offers a simple and powerful management system.

It is often said that Singlestore can meet all requirements from all of its users.

Pros:

  • just-in-time scaling
  • no downtime or offline, ability to do online alters
  • automatic sharding
  • lock-free data structures
  • hybrid OLTP and OLAP architecture

Cons:

  • relatively new database having been available since 2012
  • not fully ANSI SQL compatible as seen with databases like Oracle as well
  • works with a database optimizer

Power of Analytics Using Singlestore Conclusion

Modern businesses must use these technologies to gain a competitive advantage in their markets. Using Singlestore is the best way to have a highly-distributed, highly-scalable SQL database that can run virtually anywhere. One of the best ways to implement Singlestore is to speak to one of our experts at Data Sleek. We have completed many Singlestore projects for our clients. If you are interested in learning how Singlestore can help your business, navigate our Contact Us page and send us a little more information.

We look forward to helping you grow your business!

Data Terminology You Need to Know

Data Solutions can be a very broad term – encompassing a lot of moving parts. As a whole it is an umbrella term that covers a variety of solutions to make your data work better for you. The purpose of these solutions are to provide businesses with a way to facilitate the influx of data received into actionable data. Like its name suggests, actionable data helps you know how to formulate business plans, marketing efforts, and helps you manage customer databases – the use cases are endless. Every business has data – but does your data work for you?

New to the data world? We understand – we’ve compiled a list of data terms and explanations you need to know if you’re just starting out.

Actionable data – information that can be acted upon or information that gives enough insight into the future that the actions that should be taken become clear for decision makers.

API (Application program interface) – a set of instructions on how to access and build web-based software applications.

Big data – This refers to the vast amounts of structured and unstructured data that can come from a myriad of sources. Small data can be managed more easily, tying in with the idea presented by Allen Bonde that “big data is for machines; small data is for people”.

Big Data Scientist – Someone who can develop the algorithms to make sense out of big data.

Business Intelligence (BI) – The general term used for the identification, extraction, and analysis

Dashboard – A graphical representation of the analyses performed by the algorithms

Data aggregation – The act of collecting data from multiple sources for the purpose of reporting or analysis.

Data architecture and design – How enterprise data is structured. The actual structure or design varies depending on the eventual end result required. Data architecture has three stages or processes: conceptual representation of business entities. the logical representation of the relationships among those entities, and the physical construction of the system to support the functionality.

Database – A digital collection of data and the structure around which the data is organized.

Database administrator (DBA) – A person, often certified, who is responsible for supporting and maintaining the integrity of the structure and content of a database.

Data cleansing – The act of reviewing and revising data to remove duplicate entries, correct misspellings, add missing data, and provide more consistency.

Data collection – Any process that captures any type of data.

Data integrity – The measure of trust an organization has in the accuracy, completeness, timeliness, and validity of the data.

Data migration – The process of moving data between different storage types or formats, or between different computer systems.

Data mining – The process of deriving patterns or knowledge from large data sets.

Data science – a discipline that incorporates statistics, data visualization, computer programming, data mining, machine learning, and database engineering to solve complex problems.

Data Visualization – the graphical representation of information and data.

Data warehouse – a digital repository where businesses store their data for the purpose of reporting and analysis.

Encryption – The conversion of data into code to prevent unauthorized access.

SingleStore (formerly MemSQL) – a distributed, relational, SQL database management system known for speed in data ingestion, transaction processing, and query processing.

Metadata – Data that describes other data. This information is used by search engines to filter through documents and generate appropriate matches.

MySQL – most popular open source database. Mysql has different variants : MariaDB, Aurora, Percona and more. For each of them, the main engine is the same: innodb

RDS Mysql – A full managed database in AWS. Backup, restore, replication are handled via a few clicks via a browser interface.

Python – a general-purpose coding language. Unlike HTML, CSS, and JavaScript, it can be used for other types of programming and software development besides web development. It can handle a large range of tasks and is considered a very beginner-friendly language.

SaaS – Software-as-a-service – a software distribution model that allows a service provider to deliver applications to a customer via the internet.

Systems of record – Transactions, highly stateful, which demand absolute consistency and transactional integrity regardless of the value of an individual transaction (the state of an airline seat, for example, must be exact and must show consistently to every querying entity).

Tableau – software that can be used for data visualization.

At Data-Sleek we understand how daunting the data world can seem when you’re first introduced to it. We’re here to help you navigate your options and build customized solutions based on your unique business and needs.

Why Data Integration is critical for Small and Medium E-Commerce Businesses?

Is your eCommerce data siloed? Do you need a big picture view of operational data for increased productivity? Do you have to run multiple reports, on various platforms to get a holistic view of your business operations? Do you have to export data, just to re-import it into Google Sheets or Excel to reconcile?

If so, you’re in luck. Today, there are solutions to these challenges which eliminate the need to develop costly, in-house tools traditionally only available to large corporations.

First, let’s talk about all the data you might be underutilizing. Everyday, as a small business, you use social media to interact with your client or customer base. You might use Instagram shopping to help drive sales or use email marketing to spread the word of upcoming sales or to retarget audiences. You have a website and/or an online store to sell your products; important data is coming in via your website host and Google analytics. Every puzzle piece of your data is important and understanding it in-depth helps you drive sales and increase your profit margins!

The average business has access to 25% of their data, meaning 75% is inaccessible or hidden. What could you do with better access to your data? Not knowing the best way to read, analyze, and utilize the hidden data could be costing you! So let’s discuss the value of accessing this data, how you can, and how it can benefit your business. Data in silos (inaccessible data) or otherwise hidden, can easily be retrieved and centralized with data integration.

The Benefits of Data Integration for Small Business:

Data Integration is the process of combining data from several different sources in a unified view.
Data Integration consist of the following steps:
Data is extracted from files, databases and API endpoints and centralized in a data warehouse.Data is cleansed and modeled to meet the analytics needs of various business units.Data is used to power products or generate business intelligence.

The Benefits of Cloud Data Warehousing for Small Business:

Very beneficial to a small business has been the rise of cloud based data warehousing. As we touched on above, siloed data can be exceedingly difficult to report on. The solution is storing all data in one place where it is accessible to become a single source of truth. This allows the data to be safely stored in one location, reported on, and utilized at unlimited scale without loss of query performance.

Explore your hidden e-commerce data

The average business has access to 25% of their data, meaning 75% is inaccessible or hidden. What could you do with better access to your data? Not knowing the best way to read, analyze, and utilize the hidden data could be costing you! So let’s discuss the value of accessing this data, how you can, and how it can benefit your business. Data in silos (inaccessible data) or otherwise hidden, can easily be retrieved and centralized with data integration.

Benefits of Improved data for business function:

  • Sales Analytics
  • Customized Promotions or Offers
  • Inventory Management
  • Predictive Analytics
  • Forecasting & Market Trends
  • Retargeting/Reengaging Customers

Sales Analytics for Customized Approach to Marketing and Driving Sales

There is a lot of power in your data. Sale-related data is the most valuable asset you have. Having a clear picture of the analytics from all of your sales funnels, through all sales channels, and any marketing efforts is extremely important for the success and growth of your business. Instead of spending time out of your busy week to dig through the disjointed analytics reports from social media, website(s), and email marketing, having it all in one place streamlines your time spent reviewing this key information! Imagine knowing from just one report when and where to send promotion and offers, who is your
most valuable client group, how to reach them and predict future sales and inventory needs. Sounds like a dream come true, right?

Data for Inventory Management

Inventory or stock are the goods and materials that a business holds for the goal of resale and is the core of any commerce-based business. Predicting supply and demand unique to your business can be tricky. What trends will help you sell your product? What fluctuations in season, holidays, or shelf life impact your sales? Getting great data on all of your inventory can also tell you where to cut costs – thus, freeing up space for items that are performing really well with your clientele.

Predictive analytics based on past data makes all the difference in future sales. Being able to spot the fluctuations in your business through your data means that you can adjust the budgeting, inventory, and staffing according to when it is needed – which saves you time and money. In case of unexpected problems to do with weather, environment, or other uncontrollable factors, data forecasting can help you adjust your sales strategy effectively. Market Trends are used in almost every area of business today. Accurate analysis of consumer trends can pinpoint your development process in the creation of relevant products in tune with the market helping ensure success and sales. Using past sales data and determining future trends and growth can help establish that you have the right inventory to the right scale, to increase sales.

Retargeting and Reengaging Customers

It is a lot more cost effective to retain clientele than it is to source new customers. On average, it costs five times more to attract new clients than to retain existing ones. The probability of selling to an existing customer is 60-70% versus 5-20% of selling to a new lead. Using your data to streamline the process and reengaging with current clientele is immeasurably powerful. Getting a system in place that gives you all the data analytics from all channels helps you make informed, business-minded decisions. It helps you create perfect strategies with lasting effects where you can retarget and keep customer loyalty and increase your revenue.

In Conclusion

Data Solutions can make all the difference in the functionality of how your data works for you. Think of this as a big circle – the more you understand your data, the better your data architecture is set up, the more you can visualize your data and how it can work for you.

Talk to us today about making your data the best it can be!