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s3-lakehouse

s3-lakehouse

Guides awswrangler S3 object, file-format, dataset, S3 Tables, Delta Lake, and S3 Vectors workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: s3-lakehouse description: "Guides awswrangler S3 object, file-format, dataset, S3 Tables, Delta Lake, and S3 Vectors workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

S3 Lakehouse

Use this sub-skill when the user is working with wr.s3.* workflows.

Typical triggers:

  • "write a parquet dataset to S3"
  • "read partitioned S3 data"
  • "copy or delete S3 objects"
  • "write Delta Lake data"
  • "create an S3 Table / Iceberg table"
  • "use S3 Vectors"
  • "read Excel or CSV files from S3"

Read references/api-reference.md for the function map and optional extras. Read references/workflows.md for common end-to-end patterns. Read references/troubleshooting.md for S3 path, format, dataset, vector, and optional-dependency failures. Use ../../scripts/check_runtime.py from the root skill when you need to confirm importability or extras before a workflow.

Include here

  • S3 object lifecycle helpers such as list, upload, download, copy, delete, describe, and wait.
  • Tabular file workflows for Parquet, CSV, JSON, ORC, FWF, and Excel.
  • Dataset reads and writes with partitioning, bucketing, schema evolution, and catalog registration flags.
  • Delta Lake helpers on S3.
  • S3 Tables / Iceberg helpers.
  • S3 Vectors bucket, index, ingest, list, get, delete, and query helpers.

Exclude or route elsewhere

  • Glue Catalog metadata management and Athena SQL execution go to catalog-and-query.
  • Runtime, extras, and Ray/Modin mode questions go to runtime-and-config.
  • Redshift, Data API, and direct database connector workflows go to sql-connectors.
  • DynamoDB, Timestream, OpenSearch, Neptune, QuickSight, CloudWatch, EMR, STS, Secrets Manager, and Chime go to service-integrations.

Common user outcomes

A future agent should be able to:

  • move files between S3 locations,
  • serialize and read pandas data as S3-backed datasets,
  • register dataset metadata when the user also wants Glue/Athena visibility,
  • manage Iceberg-style S3 Tables,
  • and stage or query vectors without reopening the repository.

Shared references and scripts

  • references/api-reference.md
  • references/workflows.md
  • references/troubleshooting.md
  • scripts/smoke_s3_moto.py
  • scripts/smoke_s3_vectors_mocked.py
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