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    Fix Databricks: databricks optimize query checklist

    This usually indicates avoidable scan, join, or compute overhead in the current SQL shape.

    Why this happens

    Complex join graphs, broad SELECTs, and low-selectivity predicates can drive high cost in Databricks.

    How to fix it

    1. Reduce scanned data early with targeted filters and explicit columns.
    2. Review join predicates and remove unnecessary DISTINCT/UNION operations.
    3. Run an automated lint pass to catch anti-patterns quickly.

    Automate this with BRYCK LABS

    Instead of repeating manual checks, run this through the BRYCK LABS Snowflake / Databricks SQL Optimizer to validate and fix faster.

    Open Snowflake / Databricks SQL OptimizerBrowse All Tools