القائمة
Engine-agnostic guidance for working with the oleander lake catalog, including naming conventions, catalog hierarchy, and table reference patterns.
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استنادا إلى تصنيف SOC المهني
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.
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.
| name | lake-catalog |
| description | Engine-agnostic guidance for working with the oleander lake catalog, including naming conventions, catalog hierarchy, and table reference patterns. |
Use this skill when reading from or writing to the oleander lake catalog from Spark, Polars, SQL, or another engine.
Tables are addressed as catalog.namespace.table:
oleander for the managed Lakekeeper-backed catalogdefault, san_francisco, my_org)By default, default and telemetry namespaces are available out of the box. The telemetry namespace contains oleander-managed data you can query directly (for example oleander.telemetry.run_events, oleander.telemetry.traces, and oleander.telemetry.logs).
Examples:
oleander.default.sf_311
oleander.san_francisco.district_stats
oleander.my_namespace.results
Always use fully qualified table names through the engine's catalog integration. Do not build raw storage paths such as S3 URIs when reading or writing Iceberg tables.
Good patterns:
oleander.default.sf_311 through your engine's catalog-aware table APIoleander.my_namespace.resultsAvoid:
s3://.../warehouse/...Using the catalog name keeps Iceberg metadata, lineage, and table semantics consistent across engines.
This skill defines the shared catalog rules. Engine-specific syntax and operational advice should live in engine-specific skills.
Use companion skills when available:
spark-lake-catalog for spark.table(...), write modes, and caching guidancecatalog.namespace.table names everywhere user input, docs, and examples refer to tables.