What you’ll learn
- Gain a deep understanding of Apache Iceberg’s architecture, its role in the modern data lakehouse ecosystem, and why it outperforms traditional table formats li
- Learn how to create, manage, and query Iceberg tables using Python (PyIceberg), SQL interfaces, and metadata catalogs — with practical examples from real-world
- Build high-performance batch and streaming data pipelines by integrating Iceberg with leading engines like Apache Spark, Apache Flink, Trino, and DuckDB.
- Explore how to use cloud-native storage with AWS S3, and design scalable Iceberg tables that support large-scale, distributed analytics.
- Apply performance tuning techniques such as file compaction, partition pruning, and metadata caching to optimize query speed and reduce compute costs.
- Work with modern Python analytics tools like Polars and DuckDB for fast in-memory processing, enabling rapid exploration, testing, and data validation workflows
Welcome to Data Lakehouse Engineering with Apache Iceberg: From Basics to Best Practices – your complete guide to mastering the next generation of open table formats for analytics at scale.As the data world moves beyond traditional data lakes and expensive warehouses, Apache Iceberg is rapidly becoming the cornerstone of modern data architecture. Built for petabyte-scale datasets, Iceberg brings ACID transactions, schema evolution, time travel, partition pruning, and compatibility across multiple engines — all in an open, vendor-agnostic format.In this hands-on course, you’ll go far beyond the basics. You’ll build real-world data lakehouse pipelines using powerful tools like:PyIceberg – programmatic access to Iceberg tables in PythonPolars
