feat(data-engineering): add data-engineering extension family#266
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Introduce opt-in data-engineering extensions for AIDLC: baseline (cross-cutting data pipeline rules), catalog (dataset registration and discovery), cicd (version control and promotion), orchestration (MWAA and Step Functions), redshift (provisioned and Serverless), s3-lakehouse (Iceberg/Delta on S3 with Glue Catalog), and glue-etl (Spark, Streaming, Ray, Python shell, interactive sessions, Studio, DataBrew). Each extension follows the rule + opt-in file pattern with P0/P1/P2 severity tiers and AIDLC-stage-keyed verification.
jleto
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a team,
Kalindi-Dev,
harmjeff,
leandrodamascena,
raj-jain-aws,
scottschreckengaust and
spraja08
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Summary
aidlc-rules/aws-aidlc-rule-details/extensions/data-engineering/with seven extensions:baseline,catalog,cicd,orchestration,redshift,s3-lakehouse, andglue-etl.<name>.md) plus an opt-in prompt file (<name>.opt-in.md) with A/B/X format and[Answer]:tag.baseline, and verification bullets keyed to AIDLC stages (Requirements Analysis, Functional Design, Infrastructure Design, Code Generation, Build and Test, NFR Requirements).glue-etlcovers Glue as a compute surface (Spark, Spark Streaming, Ray, Python shell, interactive sessions, Studio, DataBrew) — complementing existing catalog/lakehouse extensions that govern Glue Data Catalog and Lake Formation.Test plan
*.opt-in.mdfile is discovered by the extensions loader at workflow startbaselineplusglue-etland validates the rules surface in Requirements Analysiss3-lakehouseS3LH-02 referenced byglue-etlGLUE-05) resolve as expectedBy submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of the project license.