Databricks & Snowflake automated optimization

Leverage tried and true optimizations for compute, queries and data storage. Click to apply, or select workloads to fully automate.

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.

Passive Recs for Databricks

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

Autonomous optimization

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

Continuous optimization

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

Query Advisor

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

Additional features

Advanced ML models

Self-improving ML models improve optimizations each run.

Visualizations

See usage and spend, allocate costs, and glean insights.

Governance

Slingshot was built by Capital One for enterprise use.