菜单
Guides agents through AWS-native data catalog and lake governance workflows. Use when designing or reviewing Glue Data Catalog, Lake Formation permissions, governed sharing, metadata quality, and access boundaries for S3, Athena, Redshift, EMR, or Glue pipelines.
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基于 SOC 职业分类
| name | glue-data-catalog-and-lake-formation-governance |
| description | Guides agents through AWS-native data catalog and lake governance workflows. Use when designing or reviewing Glue Data Catalog, Lake Formation permissions, governed sharing, metadata quality, and access boundaries for S3, Athena, Redshift, EMR, or Glue pipelines. |
Use this skill when AWS governance is anchored in Glue Data Catalog and Lake Formation, not only in generic metadata tools. It helps agents design catalog structure, access controls, governed sharing, and publish-safe dataset access across lake and warehouse workflows.
Glue Data Catalog database or table organizationLake Formation permissions, tag-based access, or sharing boundariesS3, Athena, Glue, EMR, or RedshiftDo not treat Lake Formation and the catalog as only platform-admin setup. They are part of delivery design.
Define the governed asset boundary. Clarify which datasets, tables, zones, and consumers need AWS-native governance.
Design the catalog structure. Decide:
Define the access model. Include:
Align publish behavior with governance. Require:
Validate operational behavior. Check how permissions, schema evolution, new partitions, and cross-service access behave under real delivery conditions.
| Rationalization | Reality |
|---|---|
| "IAM alone is enough." | Dataset governance often needs finer-grained sharing, tagging, and lake access patterns than broad service-level IAM. |
| "We can clean up catalog metadata later." | Poor metadata and unclear ownership make governed data hard to discover and trust. |
| "Lake Formation is only for the platform team." | Producers still need to design publish boundaries and access assumptions around it. |
Lake Formation permissions are bolted on after publish designLake Formation permissions and sharing boundaries are explicitForces replay-safe rollout plans, reconciliation gates, and rollback paths before executing any data backfill or historical reprocessing. Use when running /backfill, rerunning pipelines, repairing publish windows, or any work that risks double-counting or downstream corruption.
Helps agents classify data engineering work, choose the right preset and skill bundle, and pick the safest next command. Use when starting a session, triaging an ambiguous request, or deciding how to proceed.
Guides agents through resiliency testing for data platforms. Use when designing or running failure drills, recovery validation, failover tests, replay-safety checks, dependency outage exercises, or fault injection for pipelines and publishes.
Guides agents through production data incidents. Use when a pipeline fails, publishes bad data, misses an SLA, partially loads, corrupts state, or requires rollback, replay, or stakeholder communication.
Enforces production Kafka guardrails including non-breaking schema evolution, dead-letter queues for poison messages, and acks=all producer durability. Use when designing or changing Kafka topics, producers, consumers, schema registry policies, or streaming recovery paths.
Guides agents to wire Model Context Protocol servers for live data platform observability including Spark execution plans, OOM diagnosis, Kafka consumer lag, and orchestration run state. Use when agents need cluster metrics, streaming lag, or job diagnostics instead of blind code changes.