Architecting an Apache Iceberg Lakehouse is a practical guide that helps to deepen understanding of how to design a modern, efficient, and open lakehouse architecture based on Apache Iceberg. The material focuses on real-world scenarios, best practices, and key tools of the ecosystem.
Core Concepts and Benefits of Apache Iceberg
The book details how using the open table format Iceberg allows for creating a flexible, manageable, and scalable analytics platform without reliance on proprietary solutions like Snowflake, BigQuery, or Redshift.
- Support for ACID transactions
- Schema evolution without downtime
- High-performance analytical queries
- Compatibility with multiple engines (Spark, Trino, Flink, Presto, Hive)
Designing a Modern Lakehouse Architecture
Author Alex Merced demonstrates step-by-step an approach to creating a modular architecture based on Apache Iceberg. The reader will learn how to integrate key data processing tools into the lakehouse ecosystem and ensure data integrity, observability, and quality control.
Integration with Analytical and Computational Engines
Technologies considered include:
- Apache Spark for batch processing and ETL
- Apache Flink for streaming and real-time pipelines
- Dremio and Polaris for accelerated analytics
Strategies for Reliable Data Pipelines
Special attention is given to building reliable data loading processes — from batch processing to continuous streaming. Principles of security, versioning, storage optimization, and ensuring high performance at scale are described.
Practical Mini-Project Lakehouse
The book includes a practical exercise: creating a small lakehouse, where the reader:
- loads sales and marketing data from PostgreSQL into Iceberg tables using Spark
- optimizes the table structure for analytical queries
- visualizes results in interactive dashboards using Apache Superset
This approach allows not only to study architectural principles but also to see them in action in a real environment.
Who the Book is For
The guide will be useful for architects and data engineers familiar with the principles of the lakehouse approach and seeking to deepen their skills in designing modern analytical platforms based on open technologies.
