lezwon/CatalystOps
Semantic cost-linting and performance warnings extension for Databricks in VS Code
This tool helps data engineers and scientists who write PySpark code for Databricks identify and fix performance and cost issues early. It takes your PySpark notebook code as input and provides immediate feedback on potential problems like inefficient data operations or schema mismatches, helping you write more optimized and cheaper Spark jobs.
Use this if you are a data engineer or data scientist developing PySpark applications on Databricks and want to prevent common performance bottlenecks and control cloud spend before deploying your code.
Not ideal if you are not using PySpark, Databricks, or if you prefer to debug performance issues only after jobs have run in production.
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TypeScript
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Last pushed
Mar 27, 2026
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