Data Lakes on AWS
Designing a cost-effective lakehouse with S3, Glue and Athena
By Houssam Kodad
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About this book
What's inside
AWS gives you a hundred ways to build a data platform and very little guidance on which to pick. This book lays out an opinionated, cost-effective lakehouse on S3, Glue and Athena, with Lake Formation for governance and open table formats for reliability. You'll learn the partitioning, file-format and security decisions that determine whether your lake stays fast and cheap or quietly becomes a swamp.
What you'll learn
Skills you'll walk away with
- Lay out S3 for performance, cost and lifecycle
- Catalog data with Glue and crawlers you can trust
- Query at scale with Athena and partition projection
- Adopt open table formats like Apache Iceberg
- Govern access with Lake Formation and IAM
- Build ETL with Glue jobs and orchestration
- Keep query and storage costs under control
Table of contents
9 chapters-
01
A Lakehouse on AWS
- · Lake vs warehouse vs lakehouse
- · The S3-Glue-Athena core
- · Where Redshift fits
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02
Designing S3 Storage
- · Bucket and prefix layout
- · Partitioning strategies
- · Storage classes and lifecycle
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03
File Formats and Compression
- · Parquet and columnar layout
- · File sizing and small-file pain
- · Compaction strategies
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04
The Glue Data Catalog
- · Databases, tables and schemas
- · Crawlers vs explicit schemas
- · Schema evolution
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05
Querying with Athena
- · SQL over the lake
- · Partition projection
- · Cost and performance tuning
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06
Open Table Formats
- · Why Iceberg matters
- · Upserts, deletes and time travel
- · Migration considerations
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07
ETL with Glue
- · Glue jobs and bookmarks
- · Spark on Glue
- · Orchestration with Step Functions
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08
Governance with Lake Formation
- · Fine-grained access control
- · Row and column security
- · Cross-account sharing
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09
Cost and Operations
- · Athena and storage cost levers
- · Monitoring and logging
- · A reliability checklist
This is the full chapter list — exactly what you'll receive in the PDF.
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