avatarChristianlauer

Summary

The article compares Google BigQuery and Snowflake, highlighting their similarities and differences in scalability, performance, and use case support, ultimately concluding that the choice between them depends on specific user needs and preferences.

Abstract

The provided content discusses the comparison between Google BigQuery and Snowflake, two prominent cloud data warehouses designed for handling large datasets in the era of Big Data. Both systems are noted for their strong scaling capabilities, with BigQuery offering automatic scaling within the Google Cloud Platform, while Snowflake provides more configuration options and flexibility across multiple cloud providers. Performance is deemed comparable, with quick and reliable query execution, though the article refrains from declaring a definitive winner in this category. The support for various use cases is also similar, with both platforms catering to traditional data warehousing needs, self-service BI, SQL analyses, and integration with ML services and Python Notebooks. BigQuery, however, is noted for its seamless integration with other Google services and its unique BigQuery ML feature. The conclusion emphasizes that neither platform is objectively better, and the decision should be based on the specific requirements of the user or organization, such as platform independence or preference for Google Cloud services.

Opinions

  • The author believes that the choice between BigQuery and Snowflake is not about which one is universally better but rather which one aligns better with the user's specific use case.
  • The article suggests that Snowflake's platform-independent nature and configuration flexibility could make it more appealing to users who prioritize these features.
  • Users who are already invested in the Google Cloud ecosystem or require machine learning capabilities without data movement might prefer BigQuery.
  • The performance of both systems is considered good, but the article avoids direct comparison, indicating that it may vary based on data type, regional architecture, and other factors.
  • The author points out that Google's BigLake initiative could potentially reduce Snowflake's advantage in terms of platform independence, while also competing with other data warehouse solutions like AWS Redshift and Azure Synapse.

Google BigQuery vs. Snowflake

Which Cloud Data Warehouse is the better one?

Photo by Uriel Soberanes on Unsplash

To take out a bit of suspense, I will not test both systems with certain SQL queries and show you at the end which one is faster and you or your company should buy. But I will show you the differences and where the strengths of each system lie in.

Both systems are designed to make large amounts of data easy to analyze in the age of Big Data. While BigQuery is a Google tool within the Google Cloud Platform, Snowflake has an open structure and can be operated on all major providers.

Scaling and Computing

The two systems can scale very well and can work with Big Data. While BigQuery does this automatically, Snowflake is giving you some configuration possibilities. BigQuery and Snowflake both scale very well for data volumes and query concurrency. The decoupled storage/compute architecture supports resizing clusters without downtime, and in addition, supports auto-scaling horizontally for higher query concurrency during peak hours [1][2].

Performance

In addition to scaling and concurrency between users, the performance of the individual queries is also an important factor in modern Data Warehouses. Who likes to wait minutes or even hours for results these days? As I said, I will not test individual queries here and then say what is better, since this can be rather difficult, because it can often differ depending on data type, region, architecture and lead to different results. But if you read other sources and also from my experience so far, both systems work quickly and reliably [1][2].

Support for Use Cases

Here too, both systems are on an equal footing. Both support classic data warehousing with Self Service BI and SQL analyses. But they also offer interfaces to Python Notebooks and ML Services. BigQuery might have a slight advantage here, since it can be easily combined with other Google services and even offers machine learning via SQL with BigQuery ML [3].

Conclusion

Perhaps disappointing at first, I do not say which solution is better. In all three categories the solutions are about the same. For me, it depends more on the use case. If you want a platform-independent Data Warehouse with more configuration possibilities, you might lean more towards Snowflake, while Google Cloud users, who also want to have little maintenance effort, might lean more towards BigQuery. However, it has to be said that Google has created a solution with BigLake (Read here more about it) that allows data to be analyzed via BigQuery across platforms, also in Azure or AWS. This decreases the advantage of Snowflake a bit, but also shoots against solutions like AWS Redshift and Azure Synapse. You might also be interested in the following links:

Sources and Further Readings

[1] firebolt, Snowflake vs. BigQuery. A detailed Comparison. (2022)

[2] Stitch, Snowflake vs. BigQuery: comparing cloud data warehouses (2022)

[3] Google, What is BigQuery ML? (2022)

Google
Bigquery
Snowflake
Data Science
Data
Recommended from ReadMedium
avatarHugo Lu
BigQuery Table Partitioning and clustering Tips with dbt

Partitioning and clustering in BigQuery using different Column Types

6 min read
avatarPraveen Bhushan
Optimize Cost Using BigQuery Recommenders

BigQuery works with Active Assist and provides recommendations to optimize your data storage and processing costs

2 min read
avatarYerachmiel Feltzman
How to save 90% on BigQuery storage

Hey man, stop with the click-bait titles — I can listen to you thinking. ;)

6 min read
avatarTom Ellyatt
What is Farm_Fingerprint in BigQuery, and Why Do I Love It?

Joins are one of the most resource-intensive operations in BigQuery, especially when dealing with large datasets. Over the past few months…

5 min read
avatarAbhishek Patre
BigQuery’s underrated features no one is talking about! Part 1: Table Constraints

Welcome to a captivating journey into the depths of BigQuery, where untapped treasures await! BigQuery is a serverless, cost-effective…

4 min read
avatarXiaoxu Gao
FinOps: Four Ways to Reduce Your BigQuery Storage Cost

Don’t overlook the cloud storage cost

9 min read