About the Book
Jumpstart your journey towards mastering open data architectural patterns by learning the fundamentals and applications of open table formats
Key Features
Build open lakehouses with open table formats using popular compute engines such as Apache Spark, Apache Flink, Trino, and Python
Optimize Lakehouse performance with advanced techniques such as pruning, partitioning, compaction, indexing, and clustering
Learn how to enable seamless integration, data management, and interoperability using Apache XTable
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionEngineering Lakehouses with Open Table Formats provides detailed insights into lakehouse concepts, and dives deep into the practical implementation of open table formats such as Apache Iceberg, Apache Hudi, and Delta Lake. If you are a data engineer or architect looking to understand the intricacies of open lakehouse architectures, this book is for you.
You'll start by exploring the internals of a table format and learn in detail about the transactional capabilities of lakehouses. You’ll also work with each table format with hands-on exercises using popular computing engines such as Apache Spark, Flink, Trino, dbt, and Python-based tools. The book addresses advanced topics, including performance optimization techniques and interoperability among different formats, equipping you to build production-ready lakehouses. With step-by-step explanations, you’ll get to grips with the key components of Lakehouse architecture and learn how to build, maintain, and optimize them.
By the end, you'll be proficient in evaluating and implementing open table formats, optimizing lakehouse performance, and applying these concepts to real-world scenarios, ensuring you make informed decisions in selecting the right architecture for your organization’s data needs.What you will learn
Explore Lakehouse fundamentals such as table formats, file formats, compute engines, and catalogs
Gain a complete understanding of data lifecycle management in lakehouses
Integrate lakehouses with Apache Airflow, dbt, and Apache Beam
Optimize performance with sorting, clustering, and indexing techniques
Use the open table formats data with ML frameworks like Spark MLlib, Tensorflow, and MLFlow
Interoperate across different table formats with Apache XTable and UniForm
Secure your lakehouse with access controls and ensure regulatory compliance
Who this book is forThis book is for data engineers, software engineers, and data architects who want to deepen their understanding of open table formats such as Apache Iceberg, Apache Hudi, and Delta Lake, and learn how they are used to build lakehouses. It is also a good fit for professionals working with traditional data warehouses, relational databases, and data lakes, who wish to transition to an open data architectural pattern. Basic knowledge of databases, Python, Apache Spark, Java, and SQL are recommended for a smooth learning experience.
Table of Contents:
Table of Contents
Open Data Lakehouse – a New Architectural Paradigm
Transactional Capabilities in Lakehouse
Apache Iceberg Deep Dive
Apache Hudi Deep Dive
Delta Lake Deep Dive
Catalogs and Metatdata Management
Interoperability and Data Federation
Performance Optimization and Tuning
Data Governance and Security in Lakehouse
Decisions on Open Table Formats
Real-World Lakehouse Use Cases
About the Author :
Dipankar Mazumdar is currently a Staff Data Engineer Advocate at Onehouse.ai, where he focuses on open source projects such as Apache Hudi and XTable to help engineering teams build and scale robust data analytics platforms. Before this, he worked on critical open source projects such as Apache Iceberg and Apache Arrow at Dremio. For most of his career, he worked at the intersection of data visualization and machine learning. He has also been a speaker at numerous conferences, such as Data+AI, ApacheCon, Scale By the Bay, and Data Day Texas, among others. Dipankar has a master's degree in computer science with research focused on explainable AI techniques. Vinoth Govindarajan is a seasoned data expert and staff software engineer at Apple Inc., where he spearheads data platforms using open-source technologies like Iceberg, Spark, Trino, and Flink. Before this, he worked on designing incremental ETL frameworks for real-time data processing at Uber. He is a dedicated contributor to the open source community in projects such as Apache Hudi and dbt-spark. As a thought leader, Vinoth has shared his expertise through speaking engagements at conferences such as dbt Coalesce and Hudi OSS community meetups. He has published several blogs on building open lakehouses. Holding a bachelor's degree in information technology, Vinoth has also authored multiple research papers published in journals like IEEE.
