TutorialsFinOps Cost Optimization

FinOps Cost Optimization

Learn how to use our Observability module to track exactly how much each Snowflake query or Spark shuffle is costing you, and how to optimize them automatically.

Engineering for Value

It is remarkably easy to accidentally spend $50,000 merging tables erroneously. Data Engineers must balance latency speed against execution cost mathematically. Implementing FinOps (Financial Operations) practices directly into pipeline development is paramount.

Observability & Lineage Tracking

Under your pipeline's Observability tab, you should regularly analyze the mathematical node-expense breakdown graph. In this tutorial, we will refactor a badly-made pipeline:

Identifying the Bottleneck: We'll spot a `JOIN` node taking 2.5 hours and burning excessive Karpenter auto-scaling credits due to full-table scans.
AI Intelligent Refactoring: By clicking "Optimize FinOps", the AI Copilot will recognize that the joined columns lack Z-Ordering and Partition maps in the destination destination Lakehouse.
Implementing Clusters: We'll accept the AI suggestion, transparently rewriting the Spark output phase to `PARTITION BY (year, month)` and `CLUSTER BY (user_id)`, radically dropping the subsequent join time to under 4 minutes.