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.


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.


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.

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