Snowflake vs BigQuery

It is very important to know What is data warehousing you need depending upon your business aim and requirements and choosing the right one can be tricky at times. There are many organizations that struggle to select the best data warehouse for them. So in this Snowflake vs BigQuery blog, we will be comparing the two most popular data warehouses Snowflake and BigQuery, and how to choose the best one for you. So let’s get started

Snowflake vs Bigquery - A Comparison of Cloud Data Warehouses

What is Snowflake?

As per Wikipedia, Snowflake Inc. is an American cloud-based data-warehousing company that was founded in 2012. Snowflake offers a cloud-based data storage and analytics service, generally termed "data warehouse-as-a-service". Snowflake’s Data Cloud is powered by an advanced data platform provided as Software-as-a-Service (SaaS). Snowflake enables data storage, processing, and analytic solutions that are faster, easier to use, and far more flexible than traditional offerings.

For in-depth understanding - read our Snowflake Cloud Data Warehouse Tutorial

Why use Snowflake?

Snowflake has a multi-cluster and shared data architecture, which means that they have an architecture that separates their storage and compute layer. This helps them to scale up and down automatically as demand requires without impacting performance. Their architecture features micro-partitioning. This implies that they are able to manage semi-structured and structured data. So they can manage JSON, Parque, etc natively within Snowflake and they can do that at an infinite scale. 

The most important point is that Snowflake is delivered as a service. This makes it extremely easy to use with almost zero management. Once you migrate your data into Snowflake, everything else is taken care of, there is no need to index, prune, etc, allowing customers to focus on the value within their data.

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What is BigQuery?

As per Wikipedia, BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. It is a Platform as a Service (PaaS) that supports querying using ANSI SQL. BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure.

Related Article: BigQuery Interview Questions

Why use BigQuery?

As the business grows, managing the data spread across the gazillion applications used by teams becomes hard. This, in turn, further makes it difficult to analyze the data within these systems to get meaningful insights. Often, precious engineering resources are deployed to set up a centralized data store that hosts all this data and opens the door for BI. 

By using BigQuery, developers can now get back to focusing on essential activities such as building queries to analyze business-critical data. Also, BigQuery’s REST API enables businesses to easily build App Engine-based dashboards and mobile front-ends. Companies can then truly unleash the power of this data and empower all the stakeholders of the organization to derive insights from this. 

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Snowflake vs BigQuery: Which is Better? 

So let us get started with this Snowflake vs BigQuery article

#1. Snowflake vs BigQuery: Pricing

Snowflake uses a time-based pricing model for computing resources, wherein users are charged for execution time. BigQuery uses a query-based pricing model for compute resources, in which users are charged for the amount of data that is returned for their queries. BigQuery storage is slightly less expensive per terabyte than Snowflake storage.

#2. Snowflake vs BigQuery: Architecture

Snowflake’s architecture is a hybrid of traditional shared-disk and shared-nothing database architectures. Similar to shared-disk architectures, Snowflake uses a central data repository for persisted data that is accessible from all compute nodes in the platform. But similar to shared-nothing architectures, Snowflake processes queries using MPP (massively parallel processing) compute clusters where each node in the cluster stores a portion of the entire data set locally.

With BigQuery, a serverless data warehouse, you don’t have to think about architecture — the platform manages all resources and automates scalability and availability, so administrators don’t have to make any decisions about necessary CPU or storage levels.


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#3. Snowflake vs BigQuery: Performance 

Thanks to their ability to autoscale, both Snowflake and BigQuery perform well under various load levels. You should run benchmarks using your own data, but you’ll likely find that both platforms can handle most companies’ workloads with excellent performance.

In a head-to-head test, Snowflake edged out BigQuery in terms of raw speed, with queries taking, on average, 10.74 seconds (geometric mean). Meanwhile, BigQuery clocked in at 14.32 seconds per query, on average. In other words, Snowflake was faster in these tests.

According to independent third-party benchmarks, Snowflake performance is noticeably better than BigQuery performance. However, this conclusion is not universal—there are certain situations in which BigQuery outperforms Snowflake.

#4. Snowflake vs BigQuery: Ease of Use

Both Snowflake and BigQuery fall on the “user-friendly” side of the spectrum when it comes to the question of ease of use.

On the business software review website G2, Snowflake has received average ease of use rating of 9.2 (compared to an average of 8.7 for all data warehouse solutions). On the other hand, BigQuery earns still-respectable ease of use rating of 8.2.

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#5. Snowflake vs BigQuery: Scalability

Snowflake allows users to scale their compute and storage resources up and down independently. It includes automatic performance tuning and workload monitoring in order to improve query times while the platform is running.

BigQuery, meanwhile, handles the question of scalability entirely under the hood. As a serverless offering, BigQuery automatically provisions additional compute resources on an as-needed basis in order to handle large data workloads. This makes it easy to process even petabytes of data in a matter of just a few minutes.

#6. Snowflake vs BigQuery: Security

Both Snowflake and BigQuery use AES encryption on data at rest and support customer-managed keys. Also, both rely on roles for providing access to resources.

For authentication, Snowflake allows federated user access via Okta, Microsoft Active Directory Federation Services (ADFS), and most SAML 2.0-compliant vendors. On the other hand, BigQuery allows federated user access via Microsoft Active Directory. Both support multifactor authentication (MFA), and offer OAuth 2 for authorized account access without sharing or storing user login credentials.

Snowflake offers granular permissions for schemas, tables, views, procedures, and other objects, but not individual columns. BigQuery only offers permissions on datasets, and not on individual tables, views, or columns.

#7. Snowflake vs BigQuery: Maintenance and Management

Both Snowflake and BigQuery have low maintenance. This is due to the automated management going on in the background. In Snowflake, this implies that queries are tuned and optimized in the background while you work, and the size and power of your instance are automatically rescaled to handle the changing needs.

In BigQuery, since the platform is designed to be serverless, the users will hardly even be aware of these considerations, since everything will be happening far in the background.

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I hope you found the above comparison valid and interesting. So now you know both sides of the coin and you are free to pick one as per your requirements. There is another interesting comparison of Snowflake with Amazon Redshift (Snowflake vs Redshift). You can find the link for that article here. If you have any queries related to this blog, feel free to write them in the comments section below and we will resolve them as early as possible.

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Last updated: 03 Apr 2023
About Author

Anjaneyulu Naini is working as a Content contributor for Mindmajix. He has a great understanding of today’s technology and statistical analysis environment, which includes key aspects such as analysis of variance and software,. He is well aware of various technologies such as Python, Artificial Intelligence, Oracle, Business Intelligence, Altrex, etc. Connect with him on LinkedIn and Twitter.

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