How OLake Go Handles Schema Evolution Without Breaking Your Pipeline
How OLake Go handles schema evolution into Apache Iceberg: automatic column adds, retained drops, safe type promotions, and explicit failures only where data would corrupt. Built on Iceberg's field-ID tracking, across Postgres, MySQL, MongoDB, and Kafka.
AWS DMS vs OLake Go: Choosing the Right Tool for Your Iceberg Pipeline
Compare AWS DMS and OLake Go for database-to-Iceberg pipelines: setup, CDC, schema evolution, scaling, and cost, with benchmark numbers on over 4 billion rows.
Apache Iceberg Row Lineage: Tracking Data Lineage at the Row-Level
How Apache Iceberg v3 row lineage tracks row-level changes for CDC, with a tested look at _row_id preservation across Spark 3.5 and Iceberg 1.9 vs 1.10.
Apache Iceberg Observability: Monitoring & Metrics for Data Lake Tables
How Apache Iceberg turns table metadata into a first-class observability layer, enabling proactive monitoring, anomaly detection, and automated maintenance for modern data lakes.
How to Compact Apache Iceberg Tables: Small Files + Automation with Apache Amoro™
A practical guide to fixing small-file bloat in Apache Iceberg, showing when and how to run compaction, the performance gains you can expect, and how Apache Amoro™ automates it to turn Iceberg tables into self-optimizing lakehouses.
Beyond Structured Tables: Variant and Geospatial Data in Apache Iceberg v3
Explore how Apache Iceberg v3 introduces native support for Variant and Geospatial data types, enabling unified storage and querying of structured, semi-structured, and spatial data in modern data lakehouses.