Sat. Jul 13th, 2024

Data is one of the most important factors that many modern businesses can struggle with. Data accumulates over time and through transactions and is simply ubiquitous with most digital platforms. Interactions create data and this data can accumulate to incredible amounts that can be hard to use if not handled correctly. A data silo, for instance, is an accumulation of data that a company may have that is hard if not impossible to use.

This accumulation may be due to poor management skills, advancements in technology that made certain data seemingly obsolete, or simply neglect. Either way, finding a solution for data is a large area of interest in the modern market. At the heart of these solutions to this problem are what are known as data warehouses.

What is a data warehouse?

A data warehouse is a solution to a data silo, where data can be transformed and integrated into a centralized location where it is easily accessible for company use. This data has gone through some kind of enrichment or modification method that has made it not only accessible but viable to help drive business intelligence forward.

Using data to drive business intelligence can serve as a powerful tool to help propel the industry forward by serving insights into powerful areas like marketing and performance. When you don’t have cloud-based data warehouses, it can be challenging to use the data that you naturally have on hand to derive insight.

This data can be enriched by appending relevant and related information to give greater depth of insight. This allows for a company to have a powerhouse of data to analyze and help make high-level decisions off of. When it comes to leveraging a data warehouse or adopting one, there are two contenders on the market that will most definitely catch your eye, Snowflake, and BigQuery.

Both of these brands offer a solution to companies when it comes to data warehousing and this is everything you need to know about BigQuery vs. Snowflake.

What are BigQuery and Snowflake?

There are not a lot of disparities between BigQuery vs. Snowflake. Both of them offer warehousing data as a service and both have been developed to help businesses take raw data and turn it into analytics. These softwares do a great job of taking data silos and consolidating them into centralized warehouses where the data can be easily reached. Both offer both ETL and ELT and both are well known.

Some of the biggest differences are simply in their history and in certain aspects of their maintenance like their scalability.


One of the biggest names in the game when it comes to offering businesses a data warehouse option, Snowflake is a popular choice for good reasons. Snowflake was publicly launched in 2014 and is now one of the largest names in the data warehousing industry. This is largely in part to how dependable, easy to use, and access friendly their service is.

Snowflake is cloud-based data warehousing service that can run on any of the largest cloud providers such as Azure, GCP, or AWS. Because it was specifically built to be a cloud-based service, it has been optimized to be a unique experience that sets it apart from its competition.

One of the greatest benefits of using Snowflake is that it is a SaaS (software-as-a-Service) product that was designed to have no baggage. Developed and used in the cloud means that there is little operational or management overhead. Not only that, but created to be a diverse tool, it can be used with a variety of different providers.


BigQuery is an older name in this game and predates Snowflake by four years. Google’s very own product, launched in 2010, BigQuery was actually one of the first-ever data warehousing solutions on the market. While it has changed considerably over time, it still holds as one of the top contenders on the market, however, there are some key features that set it apart.

There are two main differences in the showdown of Bigqury vs. Snowflake. The first major difference is that BigQuery, not surprisingly since it was developed as a Google platform, is used for large databases.

BigQuery was designed and is suited for big databases and large database analytics. The second key difference is that BigQuery is also specifically used for Google providers only. However, its infrastructure, ease of use, and automated scalability all make it a great option for modern businesses despite the lack of provider diversity as found in Snowflake.


When it comes to choosing which data warehousing option is right for you and your company, knowing what kind of provider you are going to work with is probably the most important. While Google’s BigQuery is a powerful tool that is well written and optimized for automation and data warehousing that really takes stress off of your team, it doesn’t work with all providers. While conversely, Snowflake may not be as advanced a product in areas like automation and scaling but is still a leading name on the market for multiple providers.


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