Use lake-query to explore your data; compare seasonal revenue, analyze product changes, count new user signups, or find whatever answers are hidden in your data.
How to submit, monitor, and configure Spark jobs on oleander using MCP, the CLI, and the TypeScript SDK.
Run Polars queries or scripts on oleander, locally or distributed, and save results to the lake catalog.
Engine-agnostic guidance for working with the oleander lake catalog, including naming conventions, catalog hierarchy, and table reference patterns.
Spark-specific patterns for reading and writing oleander lake catalog tables, including append vs overwrite, avoiding driver writes, and reusable table naming.
General Apache Spark best practices for scalable, maintainable, and performant DataFrame jobs.
oleander-specific Spark guidance for connected OpenLineage, collect() pitfalls, and environment variable usage.