Databricks & Snowflake automated optimization

Slingshot feature showing dynamic warehouse schedules for Snowflake

Passive Recs for Snowflake

Dynamic warehouse schedules for Snowflake

Slingshot analyzes historical Snowflake cost and performance data to determine the optimal rightsizing schedule for your warehouses.

Slingshot feature showing optimization recommendations for Databricks

Passive Recs for Databricks

Optimization recommendations for Databricks

Slingshot uses Databricks System Tables to provide best-practice and insight-based optimizations, e.g. spot jobs with high memory/CPU.

Slingshot feature showing autonomous optimization for Databricks Jobs

Autonomous optimization

Autonomous optimization for Databricks Jobs

Our advanced ML models automatically train on your jobs to provide custom optimizations you can apply in a click, or automate for scale.

Slingshot feature showing continuous optimization for data cloud

Continuous optimization

Continuous optimization for data clouds

Slingshot continuously monitors and optimizes your data clouds to ensure configuration remains optimal, regardless of workload changes.

Slingshot feature showing SQL query optimization

Query Advisor

SQL query optimization

Write more efficient queries. Query Advisor reviews and recommends cost and performance optimizations for Snowflake queries.

Additional features

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