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


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.


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 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.


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:


– 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 |

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.