Apache Iceberg: From Zero to Production Data Lakehouse

As organizations increasingly adopt open data architectures, Apache Iceberg—an open and interoperable table format offering the flexibility of a data lake with the reliability of a database—has become a key technology for building modern lakehouses. Discover how to set up, manage, and optimize Apache Iceberg tables for production workloads with this hands-on course. Explore core concepts like hidden partitioning, schema evolution, and data migration strategies, then move into more advanced topics including concurrency handling, write strategies, and table maintenance. This course was designed for data engineers, analytics engineers, data platform engineers, and data architects who work with data lakes and want to modernize their data infrastructure. It's also valuable for software engineers transitioning into data roles and technical leads evaluating Apache Iceberg for their organizations.

Learning objectives
Build and configure an Apache Iceberg lakehouse.
Design optimal table structures using hidden partitioning, sort orders, and column metrics to maximize query performance.
Migrate existing data from Hive tables, Parquet files, CSV, and databases into Iceberg.
Implement production workflows using Write-Audit-Publish for validation, branching for testing, and rollback for recovery.
Evolve table schemas and partition specifications without downtime or rewriting data.
Execute maintenance operations, including data file compaction, metadata compaction, and snapshot expiration.
Configure write strategies (merge-on-read vs copy-on-write) and distribution modes for different workload requirements.
Manage concurrent operations and avoid conflicts in multi-writer scenarios.