Architecting an Apache Iceberg Lakehouse (MEAP V03)

The "lakehouse" data architecture is a powerful way to combine the flexibility of data lakes with the management features of data warehouses. The open source Apache Iceberg framework delivers the scalability, reliability, and performance you want from a lakehouse without the expense and vendor lock-in of platforms like Snowflake, BigQuery, and Redshift.
In Architecting an Apache Iceberg Data Lakehouse, data guru Alex Merced shows you
How to create a modular, scalable Iceberg lakehouse architecture
Where Spark, Flink, Dremio, Polaris fit into your design
Reliable batch and streaming ingestion pipelines
Strategies for governance, security, and performance at scale
Apache Iceberg is an open source table format perfect for massive analytic datasets. Iceberg enables ACID transactions, schema evolution, and high-performance queries on data lakes using multiple compute engines like Spark, Trino, Flink, Presto, and Hive. An Iceberg data lakehouse enables fast, reliable analytics at scale while retaining the observability you need for compliance audits, governance, and provable data security.
about the book
Architecting an Apache Iceberg Data Lakehouse teaches you to design a complete data platform with Iceberg. The book carefully guides you through the architecture of your platform-from storage to governance. Each layer is fully illustrated and includes hands-on examples that connect theory with practical implementation. You'll ingest sales and marketing data from PostgreSQL into Iceberg tables using Apache Spark, build interactive dashboards in Apache Superset, design and compare ingestion pipelines, and much more. Author Alex Merced's experienced guidance helps you understand the important tradeoff decisions you'll need to make in real-world implementations. You'll soon have a scalable and maintainable data platform that can handle petabytes of data!

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