AWS

S3, Glue, Athena, Kinesis, Redshift, EMR and IAM — the most common data platform in the world. This path sequences the category's 19 resources by roadmap stage and composite score — work top-down inside each step.

Cloud

1. AWS MCP Servers (Labs) 38.5 — AWS MCP Servers (Labs)
2. AWS Glue Job Reviewer 37 — Review a Glue job for cost, DPU sizing, and bookmarks correctness.
3. Cut Athena bytes scanned with partitions and projection 34.15 — Cut Athena bytes scanned with partitions and projection
4. Hold the cloud data stack mental model — storage, catalog, compute, IAM 34.15 — Hold the cloud data stack mental model — storage, catalog, compute, IAM
5. Review a Glue job for sizing, bookmarks, and catalogue drift 34.15 — Review a Glue job for sizing, bookmarks, and catalogue drift
6. Stand up the S3 → Glue → Athena starter 34.15 — Stand up the S3 → Glue → Athena starter

13 more on the category page.

Documented prerequisites, deduplicated: skill-spark-data-quality-review.

• Size Glue workers from data volume, not habit
• Use job bookmarks correctly
• Avoid full-table reprocessing from catalogue mistakes
• Read the scanned-bytes metric per query
• Apply partition projection for high-cardinality columns
• Compact small files to cut per-request overhead
• Explain storage/compute separation with one managed example per layer
• Name the IAM surface each managed service needs before the first query

Security-flagged: AWS MCP Servers (Labs), AWS Glue Job Reviewer, Athena Partition Advisor, S3 → Glue → Athena, AWS Architecture Reviewer, Terraform AWS Data Platform — review the security posture first. Verification on these records: 19 community. Nothing here is independently audited — the trust scan and your own review are the controls.

Phase 3 of the roadmap. Stage context: Cloud · side-by-side comparison · all learning topics