Building a data platform can be difficult, and many systems are expensive to run. When you rely on one vendor, it can be hard to change or improve your setup later. A modern data platform should be reliable, able to grow with your needs, and not tied to costly licences. Open-source tools give you more control over how your platform works and how much you spend. This book shows a clear way to build a complete data platform using Apache Iceberg. 

• Learn how to create a modular and scalable Iceberg Lakehouse architecture. 

• Understand where Spark, Flink, Dremio, and Polaris fit into your design. 

• Build reliable batch and streaming ingestion pipelines for your data. 

• Apply strategies for governance, security, and performance at scale. 

• Connect architectural theory with practical implementation through hands-on examples. 

Architecting an Apache Iceberg Lakehouse is a practical guide to designing a complete data platform. The book carefully guides you through each layer of the architecture, from storage and ingestion to governance and security. It uses hands-on examples, such as ingesting data with Apache Spark and building dashboards in Apache Superset, to illustrate key concepts. 

After reading this book, you will understand how to design and build a data platform that can grow with your needs. It explains the key choices you need to make when working with large amounts of data in real projects. 

This book is written for data architects who already know the basics of data Lakehouses and are planning a new system or moving from an existing one. 

Building a data platform can be difficult, and many systems are expensive to run. When you rely on one vendor, it can be hard to change or improve your setup later. A modern data platform should be reliable, able to grow with your needs, and not tied to costly licences. Open-source tools give you more control over how your platform works and how much you spend. This book shows a clear way to build a complete data platform using Apache Iceberg. 

• Learn how to create a modular and scalable Iceberg Lakehouse architecture. 

• Understand where Spark, Flink, Dremio, and Polaris fit into your design. 

• Build reliable batch and streaming ingestion pipelines for your data. 

• Apply strategies for governance, security, and performance at scale. 

• Connect architectural theory with practical implementation through hands-on examples. 

Architecting an Apache Iceberg Lakehouse is a practical guide to designing a complete data platform. The book carefully guides you through each layer of the architecture, from storage and ingestion to governance and security. It uses hands-on examples, such as ingesting data with Apache Spark and building dashboards in Apache Superset, to illustrate key concepts. 

After reading this book, you will understand how to design and build a data platform that can grow with your needs. It explains the key choices you need to make when working with large amounts of data in real projects. 

This book is written for data architects who already know the basics of data Lakehouses and are planning a new system or moving from an existing one. 

Building a data platform can be difficult, and many systems are expensive to run. When you rely on one vendor, it can be hard to change or improve your setup later. A modern data platform should be reliable, able to grow with your needs, and not tied to costly licences. Open-source tools give you more control over how your platform works and how much you spend. This book shows a clear way to build a complete data platform using Apache Iceberg. 

• Learn how to create a modular and scalable Iceberg Lakehouse architecture. 

• Understand where Spark, Flink, Dremio, and Polaris fit into your design. 

• Build reliable batch and streaming ingestion pipelines for your data. 

• Apply strategies for governance, security, and performance at scale. 

• Connect architectural theory with practical implementation through hands-on examples. 

Architecting an Apache Iceberg Lakehouse is a practical guide to designing a complete data platform. The book carefully guides you through each layer of the architecture, from storage and ingestion to governance and security. It uses hands-on examples, such as ingesting data with Apache Spark and building dashboards in Apache Superset, to illustrate key concepts. 

After reading this book, you will understand how to design and build a data platform that can grow with your needs. It explains the key choices you need to make when working with large amounts of data in real projects. 

This book is written for data architects who already know the basics of data Lakehouses and are planning a new system or moving from an existing one.