Learnings from optimising 22 of our most expensive Snowflake pipelines
The team optimized 22 expensive Snowflake pipelines by implementing pragmatic cost-reduction strategies, improving efficiency without major redesigns.
MAIN POINTS
- Investigated and optimized 22 costly Snowflake pipelines, merging 56 changes to reduce expenses.
- Emphasized practical solutions like removing unused columns, adjusting schedules, and simplifying queries.
- Adopted strategies like time windows, modularization, and warehouse size adjustments to cut compute costs.
TAKEAWAYS
- Ensure analytics tools sync with version control systems like GitHub for efficient tracking and documentation.
- Use explicit filtering and avoid complex predicates to enhance Snowflake's query pruning efficiency.
- Apply the 20/80 rule to focus efforts on areas with the most significant potential for cost savings.