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10 Cost Optimization Tips for Snowflake

Snowflake stands out as a versatile platform that empowers users to scale their compute resources dynamically. One of its key features is the ability to right-size virtual warehouses, allowing teams to allocate resources efficiently based on demand. However, optimizing costs and maximizing performance in Snowflake requires more than just adjusting warehouse sizes. In this blog post, we’ll delve into ways Matillion can help you fine-tune your Snowflake environment for optimal efficiency. From leveraging materialized views to implementing data lifecycle management and beyond, we'll explore various strategies and best practices to help you get the most out of Snowflake (and Matillion) while keeping costs in check.

#1 Right-Size Snowflake Virtual Warehouses 

Snowflake allows you to scale your compute resources up or down based on demand. Use Matillion’s Alter Warehouse component to control the warehouse size within any given Orchestration job. Start or scale up at the beginning, spin or scale down at the end to save when the compute resources aren’t needed.

#2 Utilize Materialized Views 

A materialized view is a database object that contains the results of a precomputed query, stored as a physical table, enabling faster query performance by avoiding the need to recompute the result set each time the query is executed. Leveraging materialized views in Snowflake helps customers save costs by reducing query processing time and minimizing compute resources, as the pre-computed results stored in materialized views eliminate the need for repeated, expensive computations. Use Matillion to create and manage materialized views.

#3 Optimize Queries 

Leverage Snowflake’s Query Profile for detailed insights into a query execution. Leverage Matillion’s Task History to observe query executions and look for opportunities for iterative job design enhancements to improve runtimes.

#4 Compress Data Efficiently

Snowflake supports various compression techniques to minimize storage costs. Compression is primarily automatic in Snowflake, but several methods (e.g. COMPRESSION parameter) are available for users to control compression. Matillion’s Load components also allow users to specify compression settings as needed.

#5 Utilize Time Travel and Fail-safe

Snowflake offers Time Travel and Fail-safe features that allow you to access historical data and for disaster recovery scenarios. But note that these features consume storage credits. Review and adjust the retention periods for Time Travel and Fail-safe to balance data protection requirements with cost optimization goals.

#6 Cluster Tables Effectively 

Use clustering keys on your tables to physically organize the data, reducing the need for data sorting during queries and improving performance. Matillion’s SQL Script component enables users to execute SQL against Snowflake, allowing users to apply clustering keys via the ALTER TABLE command based on previous job results.

#7 Implement Data Lifecycle Management

Implement data retention policies to automatically purge data from Snowflake as it becomes unneeded. Matillion can assist in orchestrating and automating tasks related to data retention policies. 

  • Developers can create workflows that automate data archival or deletion processes based on schedules or triggers. 
  • Via the SQL Script component, Matillion can integrate with Snowflake Tasks to schedule and execute SQL scripts that implement data retention policies.  

#8 Enable Query Caching

Leverage Snowflake’s query caching feature to cache and reuse frequently executed queries, reducing compute costs and improving query performance for recurring workloads. Use Matillion to easily schedule recurring pipeline executions to ensure you are taking advantage of Snowflake data caches. 

#9 Sample Data During Development 

Use data sampling techniques to reduce processing time and costs during development and test phases. Matillion’s Sample feature enables developers to generate representative samples during pipeline development.

#10 Monitor and Alert on Performance 

Continuously monitor Snowflake usage and performance metrics using Snowflake’s built-in resource monitor capabilities. Resource monitors can:

  • Track warehouse utilization metrics such as CPU usage and credits consumed
  • Provide insights into query execution times and resource consumption
  • Track storage consumption by tables and databases

By leveraging Snowflake’s resource monitor feature, customers can gain visibility into their usage patterns and ultimately achieve cost savings through better management of compute and storage resources.

To Conclude

Mastering the art of optimizing costs and performance in Snowflake involves an approach that encompasses advanced features, efficient workflows, and continuous monitoring of usage patterns. With the aid of a tool like Matillion, orchestrating these strategies becomes more manageable, allowing users to automate processes and improve iteratively. With a proactive mindset towards cost optimization, organizations can successfully harness the full potential of Snowflake while staying within budget constraints.

Steph Van Handel
Steph Van Handel

Delivery Solution Architect