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Summary

Describe what you implemented.

Source Schema Design

  • Domain chosen:
  • Strong entities:
  • Weak entities:
  • Keys, relationships, and indexes:
  • Source validation rules:

CDC Strategy

  • How changes are captured:
  • How inserts, updates, and deletes are handled:
  • How replay/restart works:
  • How duplicates are handled:

Lake and Warehouse Modeling

  • How the lake captures every change:
  • How the warehouse maintains latest snapshot:
  • How time travel / restore is supported:

Schema Change Safety

  • How incompatible changes are detected:
  • How ingestion is stopped:
  • How warnings/failures are surfaced:

Validation Parity

  • Which source system validations were mirrored downstream:
  • How failures are checked and reported:

Catalog Exposure

Explain how lake and warehouse datasets are published and discovered.

Validation

List the commands or workflows you ran to validate the solution.

Known Limitations / Next Steps

List tradeoffs or improvements you would make with more time.

Responsible AI Usage

  • Did you use AI tools?
  • Where did they help?
  • What did you personally verify or correct?

Author Checklist

  • Linting passes
  • Tests pass
  • Model validation/tests pass
  • Schema compatibility checks pass
  • Data quality validations pass
  • Catalog metadata validation passes
  • README was tested from a clean setup
  • End-to-end CDC flow was validated locally