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 Description
Engineering 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 for
This 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.
