Apache Iceberg: The Beginner's Guide

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 Python
Polars – lightning-fast DataFrame library for in-memory transformations
DuckDB – local SQL powerhouse for interactive development
Apache Spark – for large-scale batch and streaming processing
AWS S3 – cloud-native object storage for Iceberg tables
And many more: SQL, Parquet, Glue, Athena, and modern open-source utilities

What Makes This Course Special?

• Hands-on & Tool-rich: Not just Spark! Learn to use Iceberg with modern engines like Polars, DuckDB, Databricks, Snowflake.

• Cloud-Ready Architecture: Learn how to store and manage your Iceberg tables on AWS S3, enabling scalable and cost-effective deployments.

• Concepts + Practical Projects: Understand table formats, catalog management, schema evolution, and then apply them using real datasets.

• Open-source Focused: No vendor lock-in. You’ll build interoperable pipelines using open, community-driven tools.

What You’ll Learn:

• The why and how of Apache Iceberg and its role in the data lakehouse ecosystem

• Designing Iceberg tables with schema evolution, partitioning, and metadata management

• How to query and manipulate Iceberg tables using Python (PyIceberg), SQL, and Spark

• Real-world integration with DuckDB, and Polars

• Using S3 object storage for cloud-native Iceberg tables

• Performing time travel, incremental reads, and snapshot-based rollbacks

• Optimizing performance with file compaction, statistics, and clustering

• Building reproducible, scalable, and maintainable data pipelines

Who Is This Course For?

• Data Engineers and Architects building modern lakehouse systems

• Python Developers working with large-scale datasets and analytics

• Cloud Professionals using AWS S3 for data lakes

• Analysts or Engineers moving from Hive, Delta Lake, or traditional warehouses

• Anyone passionate about data engineering, analytics, and open-source innovation

Tools & Technologies You’ll Use:

• Apache Iceberg, PyIceberg, Spark,

• DuckDB, Polars, Pandas, SQL, AWS S3, Parquet

• Integration with Metastore/Catalogs (REST, Glue)

• Hands-on with Jupyter Notebooks, CLI

By the end of this course, you'll be able to design, deploy, and scale data lakehouse solutions using Apache Iceberg and a rich ecosystem of open-source tools — confidently and efficiently.