Building a Complete AWS Data Lakehouse: S3 to QuickSight

End-to-end architecture and implementation course for a production AWS data lakehouse, S3 storage tiers including Glacier, open table formats (Iceberg/Hudi/Delta), Glue Data Catalog and ETL, Athena and Redshift query layers, streaming and batch ingestion, Lake Formation governance with row/column-level security, Macie-driven PII protection, orchestration, data quality, cost optimization, and QuickSight BI, with architecture diagrams, real AWS customer cases, worked pricing examples, and practice questions.

Intermediate12 Weeks17 LessonsNuniversity
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Course Content

  1. 01Lakehouse Architecture Fundamentals: Lakes, Warehouses, and the Medallion Pattern
  2. 02S3 as the Data Lake Foundation: Storage Classes, Glacier, and Durability
  3. 03Data Formats and Compression: CSV, JSON, Avro, Parquet, and ORC
  4. 04Open Table Formats: Iceberg, Hudi, and Delta on the Lakehouse
  5. 05Batch Ingestion Patterns: DMS, AppFlow, DataSync, and Transfer Family
  6. 06Streaming Ingestion: Kinesis, Amazon MSK, and Landing Streams in the Lake
  7. 07Glue Data Catalog: Crawlers, Partitions, and Schema Management
  8. 08Transformation at Scale: AWS Glue, EMR Serverless, and Lambda
  9. 09Athena as the Lakehouse Query Engine: Cost, Performance, and Iceberg SQL
  10. 10Redshift in the Lakehouse: Spectrum, Serverless, Data Sharing, and Federation
  11. 11Orchestrating the Lakehouse: Step Functions, EventBridge, and MWAA
  12. 12Lake Formation Governance: Permissions, Row/Column Security, and Data Sharing
  13. 13PII Protection on the Lakehouse: Amazon Macie, Masking, and Privacy Workflows
  14. 14Security Hardening: KMS, Least-Privilege IAM, and Network isolation
  15. 15Data Quality and Observability: DQDL, Quarantine, Lineage, and SLOs
  16. 16Cost Optimization and Lifecycle: From Hot Prefixes to Glacier Deep Archive
  17. 17QuickSight BI and the End-to-End Lakehouse Capstone

Lessons: 17 · 12 Weeks