Learn how to automate compaction, snapshot expiration, and layout optimization in Apache Iceberg using metadata-driven triggers and orchestration tools for a self-healing lakehouse.

The Endgame — Building an Autonomous Optimization Pipeline for Apache Iceberg

Learn how to scale Apache Iceberg table optimizations across large datasets using parallelism, checkpointing, and fail recovery to ensure reliability and performance.

Managing Large-Scale Optimizations — Parallelism, Checkpointing, and Fail Recovery

Partition evolution in Apache Iceberg is a powerful feature, but if not managed carefully, it can introduce fragmentation and impact compaction performance. Learn how to handle it effectively.

Hidden Pitfalls — Compaction and Partition Evolution in Apache Iceberg

Discover how to use Apache Iceberg's metadata tables to proactively detect small files, bloated manifests, and table fragmentation—so you can trigger compaction only when it's needed.

Using Iceberg Metadata Tables to Determine When Compaction Is Needed

Learn how to design an effective schedule for compaction and snapshot expiration in Apache Iceberg to balance cost, performance, and data freshness.

Designing the Ideal Cadence for Compaction and Snapshot Expiration

Learn how to design fast, incremental compaction strategies in Apache Iceberg to support high-throughput streaming pipelines without disrupting freshness or performance.

Optimizing Compaction for Streaming Workloads in Apache Iceberg

Learn how standard compaction works in Apache Iceberg and why bin packing your data files is essential for maintaining query performance and cost efficiency.