• views: 137
• date: 12 October 2025
• posted by: 0nelove

Build a production-ready Lakehouse on AWS (S3, Glue, Athena, Lake Formation) — plus Orchestration, Data Quality & AI.
What you'll learn
Design an AWS Data Lakehouse with S3 + Glue + Iceberg + Athena + Glue Catalog
Apply Data Governance using Lake Formation: LF-Tags/TBAC, PII masking, row-level security
Build a Redshift Serverless warehouse: external tables over Iceberg, star schema, SCD2 with MERGE
Operate batch pipelines: orchestrate runs, handle break/fix, idempotent replays, and backfills
Validate data with quality checks and use auditing/lineage (Lambda+DynamoDB, CloudWatch/CloudTrail)
Produce ML-ready datasets and reproducible training views via Iceberg snapshots/time travel
Requirements
Basic AWS and SQL
Some data engineering familiarity (files, partitions, schemas)
Python/PySpark exposure helps—every step is guided
Description
Build portfolio-grade AWS Cloud projects that mirror real data teams.This course is 100% hands-on. You'll design and operate a production-style Data Lakehouse on AWS, enforce Data Governance with Lake Formation, stand up a Redshift Serverless warehouse with SCD2, run a Batch Ops simulation (break/fix/backfill), and prepare AI/ML-ready datasets—exactly how modern orgs work.You will use S3, Glue (PySpark), Athena, Lake Formation, Glue Catalog, Apache Iceberg, Redshift Serverless (external & managed tables), IAM, Lambda, DynamoDB, CloudWatch/CloudTrail—with a focus on cost, reliability, and auditability.What you'll build (5 connected projects)Project 1 — Lakehouse on AWS: S3 + Apache IcebergLand RAW to S3, transform with Glue, publish Iceberg bronze/silver, implement partitioning & schema evolution, and gate publishes with data quality checks.Project 2 — Data Governance with Lake FormationEnforce tag-based policies (LF-Tags), column masking and row-level filters (Data Cells Filters). Prove access in Athena (Analyst vs Scientist). Add lightweight audit.Project 3 — Data Warehouse on Redshift Serverless (External + SCD2)Expose Iceberg via external tables, build star schema (facts/dims), implement SCD2 with MERGE, and tune performance/cost (sort/dist keys, WLM/workgroup choices).Project 4 — A Day in the Life of a Data Engineer (Batch Ops Simulation)Orchestrate ingest → DQ → publish, handle schema change / late data, rerun safely, backfill last N days, and write a clear incident postmortem.Project 5 — AI/ML Readiness & ServingCurate ML-friendly/feature-like tables, ensure reproducible training sets using Iceberg snapshots/time travel, and (optional) integrate SageMaker/Athena for model workflows.
Who this course is for
Data Engineers / Analytics Engineers building real AWS portfolio projects
AI/ML & Data Scientists who need governed, query-ready, reproducible datasets
Cloud & Platform Engineers implementing secure data platforms on AWS
Architects / Leads who want an end-to-end reference implementation
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