AWS data lake services — S3, Glue, Athena, EMR, Lake Formation, and Apache Iceberg integration on Amazon Web Services.
8 articles
S3 Tables embeds managed Iceberg into S3 with automatic compaction. Self-managed Iceberg gives full control over catalogs, engines, and maintenance. A production comparison across compaction, observability, engine support, security, cost, and the control plane that ties it all together.
Athena charges $5 per TB scanned — and on poorly maintained Iceberg tables, every query scans far more data than it should. This guide breaks down why Athena bills explode on Iceberg (small files, bad sort order, stale manifests, scan amplification) and presents two paths to fix it: autonomous optimization with LakeOps or the manual approach with Athena SQL and Spark.
A practical guide for migrating Apache Iceberg catalogs — from Hive Metastore to REST (Polaris, Gravitino), AWS Glue, Nessie, or Unity Catalog. Covers in-place metadata registration, dual-catalog access, validation, rollback strategies, and multi-catalog federation with LakeOps.
Orphan files are invisible to Iceberg but fully billable by cloud storage. They accumulate silently from failed writes, crashed compaction, and concurrent conflicts — and on mature lakes they can account for 25–40% of storage spend. This guide covers how orphan files are created, how to detect them safely, the retention window that prevents table corruption, and how to automate cleanup at lake scale without listing millions of objects.
AWS Glue provides native Iceberg support for cataloging, ETL, and built-in table maintenance — but production lakehouses hit limitations fast. This guide covers Glue catalog configuration, ETL best practices, compaction tuning, common pitfalls, and how a dedicated control plane fills the operational gaps.
Apache Iceberg on AWS S3 is the standard architecture for open lakehouses. This guide covers how Iceberg's metadata hierarchy maps to S3 objects, the AWS services ecosystem (Glue, Athena, EMR, Redshift, S3 Tables), configuration best practices, performance optimization, table maintenance, and the operational components needed for production deployments.
AWS S3 bills for Iceberg lakehouses are inflated by small files, orphan data, retained snapshots, metadata overhead, and scan amplification. This guide quantifies each cost vector with S3 pricing mechanics and walks through five strategies — compaction, expiration, layout optimization, storage tiering, and engine routing — to cut storage and query spend.
See how a 350TB data lake shrank to 230TB in 10 minutes by removing stale data—saving 34% in AWS S3 costs and proving the need for a control plane.
