Snowflake vs BigQuery: Complete Guide to Know the Differences
Data warehouse platforms are playing a vital role in fulfilling the data and analytics needs of modern businesses. A data warehouse platform sources business information from a wide range of areas internally and externally and provides data to BI tools. There are different cloud data warehouse solutions available in the market and are designed to address modern business requirements.
Each data warehouse platform is unique and choosing the right one is a challenging task. It is a well-known fact that the majority of the data warehouse projects fail due to multiple factors like lack of institutional buying, choosing the wrong technology, poor cost and time estimates, etc.
There are some major cloud data warehouse players in the market which includes Snowflake, Amazon Redshift, Google BigQuery and Microsoft. In this blog, we are going to compare Snowflake vs BigQuery using different parameters. Let’s dive into the details part.
Following are the concepts covered in this Tutorial:
Table of Contents
What is a Cloud data warehouse?
A Cloud data warehouse is an advanced technology, which uses computing power and space from a cloud provider to extract and store business information from a wide range of data sources. It facilitates data storage, analysis and reporting.
What is Snowflake?
Snowflake is a cloud-based data warehouse platform offered as a SaaS (Software as a Service). It offers a unique solution that separates computation from storage. Snowflake offers an automatic scalability feature that instantly scales up and down based on requirements which aim at improving performance and minimization of cost.
Snowflake comes with advanced architecture and uses a powerful SQL engine. It is fast, reliable and easy to use compared to traditional offerings. It stores both structured and unstructured data and makes it compatible with SQL.
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What is Google BigQuery?
Google BigQuery is a fully managed and advanced enterprise data warehouse system that allows organizations to solve data management problems by enabling fast SQL queries using Google’s processing infrastructure. Using Google BigQuery you can easily conduct analysis on a petabyte of data with the help of ANSI SQL integration with multiple applications.
Google BigQuery comes with the capabilities to process billions of rows in seconds and gives you real-time analytics of streaming data. The unique features and simplified configuration process have set it apart from all other Data Warehouse tools and made it a widely deployed tool.
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Major Differences between Snowflake and BigQuery
1) Snowflake vs BigQuery Pricing:
Snowflake Pricing:
The Pricing of Snowflake is directly proportionate to the usage. As Snowflake separates storage and computing it charges separately for both.
Storage pricing: $23 per terabyte per month if paid upfront, and for on-demand $40 per terabyte per month.
Compute Pricing: Snowflake offers seven different tiers for storage and the pricing is a bit complex. The cheapest plan is Standard edition and this costs $2 per hour or one credit per hour.
To have a clear idea of Snowflake pricing you can check out here
BigQuery Pricing:
BigQuery follows the same pattern as Snowflake in terms of charging its customers. It also separately charges for storage and computing.
Storage Pricing: $20 per terabyte per month for active storage, and $10 per terabyte per month for long-term storage. The best thing about BigQuery is that it offers free 10 gigabytes of data a month for free.
Compute Pricing: It uses a query-based pricing model to offer to compute resources. Google charges for on-demand queries at a price of $5 per terabyte. As bigQuery charges based on the data returned not on the number of hours. So, it’s a bit challenging to estimate costs. The advantage is that it freely offers the first terabyte of queries each month.
To have a clear idea of BigQuery pricing you can check out here.
2) Snowflake vs BigQuery: Performance:
Based on a series of tests conducted by a technology blog GigaOm, Snowflake has outperformed all other data warehouse platforms which include Amazon Redshift, Google BigQuery, and Azure data warehouse SQL.
To complete all 103 TPC-DS queries snowflake took 5,793 seconds whereas BigQuery has taken much extra which is 37,283 seconds. In Query 44 of the benchmark tests, BigQuery has outperformed Snowflake.
We can not decide which is best or which one lacks. Both Snowflake and BigQeiy databases are constantly evolving to new heights and trying their best to provide better performance to their customers. One may cross others in different areas of performance because of adding new features and constant developments.
3) Snowflake vs. BigQuery: Security
Security is a very essential factor to consider data warehouse platforms as sometimes there is a need to store and process sensitive data like healthcare, financial, retail, etc. The best thing is that both Snowflake and Google bIgquery offer high-security features to secure data.
To enable secured access to the users’ Snowflake uses Microsoft active directory services (ADFS), Okta, and many SAML 2.0 – complaint tools. Snowflake supports multi-factor authentication (MFA), IP whitelisting and blacklisting, Automatic data encryption, and support OAuth and user SSO.
Google BigQuey and Google Cloud automatically encrypt the data for both the data in transit and the one at rest. BigQuery uses Cloud Identity and Access Management (IAM) to control user access to cloud resources. BigQuery also supports multi-factor authentication (MFA).
4) Snowflake vs. BigQuery: Scalability
Snowflake offers automatic scaling features. In snowflake, users have the chance to scale up and down their compute and storage resources according to the requirements. The automatic performance tuning and workload monitoring feature of snowflake helps in improving query performance during run time.
BigQuery also offers advanced scalability features to meet the growing data needs of businesses. Using the power of its Serverless offering, BigQuery automatically supports additional resources to meet data workloads. This helps the organizations in processing large volumes of data in a span of minutes.
5) Snowflake vs. BigQuery: Ease of Use
Snowflake and Big query both offer advanced and easy-to-use interfaces. When it comes to Snowflake the interface is quick and easy to manage to compute clusters on it. Google BigQuery offers a serverless, fully managed data warehouse platform and requires no lengthy setup or configuration.
Closing Thoughts
Cloud Data warehouse platforms are playing a crucial role in today’s data-driven world. Snowflake and BigQuery both are widely deployed data warehouse platforms and offer unique features. Choosing the right data warehouse platform will help you in taking the right business decision and thereby help the business grow. You can also check out our Snowflake interview questions and answers blog here. Happy learning!
Author Bio
Yamuna Karumuri is a content writer at CourseDrill. Her passion lies in writing articles on the IT platforms including Machine learning, Workday, Sailpoint, Data Science, Artificial Intelligence, Selenium, MSBI, and so on. You can connect with her via LinkedIn.
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