Top 5 performance best practices with Snowflake
By Mike Sargo
Chief Data and Analytics Officer and Co-Founder of Data Ideology
The Snowflake Data Platform is designed for scale, efficiency, and ease of use.
It supports an unlimited number of Virtual Data Warehouse clusters that offer shared access for optimal performance. The Snowflake Data Platform requires almost zero management or tuning as there are no indexes and only a few options available to tune the database platform. Undoubtedly, Snowflake was designed for simplicity and offers almost no performance tuning options. Instead of describing technical tuning options, this article summarizes the top five best practices to maximize query performance.
The data platform itself doesn't support many options for performance tuning. Nonetheless, best practices make a difference in improving query performance.
Here are the top 5 performance best practices for the Snowflake data platform:
One of the best ways to maximize performance during data loading is to optimize the files' size. Make sure to:
- Split the data into multiple small files to support optimal data loading in Snowflake.
- Use a separate data warehouse for large files.
The number and capacity of the servers determine the number of data files.
Snowflake caches data in the virtual data warehouse, but it's still essential to segment data. Consider these best practices for data query performance:
- Group users with common queries in the same virtual data warehouse to optimize data retrieval and use.
- The Snowflake Query Profile supports query analysis to help identify and address performance concerns.
Snowflake draws from the same virtual data warehouse to support complex data science operations, business intelligence queries, and ELT data integration.
Snowflake allows for a scale-up in the virtual data warehouse to better handle large workloads. When using scale-up to improve performance, make note of the following:
- Snowflake supports fast and easy adjustments to the warehouse-size to handle the workload.
- It can also automatically suspend or resume the scale-up, with complete transparency for the user.
Snowflake's scale-up functionality supports the continually changing requirements for processing.
Snowflake supports the deployment of same-size clusters to support concurrency. Keep these points in mind for how scale-out can help performance optimization:
- As users execute queries, the virtual data warehouse automatically adds clusters up to a fixed limit.
- It can scale-up in a more controlled way instead of deploying one or more clusters of larger machines like legacy data platforms.
Snowflake automatically adjusts based on user queries, with automatic clustering during peak and off hours as needed.
Without proper oversight and planning, database design and development features can become a nightmare. Here are the best practices for database design:
- Plan for any changes in advance, and meet with the team to map out your data model.
- Configure and test in the dev system to avoid untested deployment.
- Communicate with the team to make sure everyone is on the same page.
With proper planning and communication, Snowflake should not experience any design issues.
Implement Best Practices to Improve Performance with Snowflake
Snowflake is designed to be efficient and effective, so it's sometimes easy to overlook the simple yet best practices to improve performance. As you can see, these top best practices can improve performance with Snowflake.
At Data Ideology, we will work with you to determine the best practices and how to implement them as part of your data engineering process with Snowflake's data platform. We help you work smarter, not harder.
Written by Mike Sargo
Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.
Snowflake vs Azure E-Book
Data Ideology has created a Free Comprehensive E-Book that highlights many of the key differences, advantages and disadvantages to consider when starting your Cloud Data Migration Journey.