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  • Book Overview & Buying Engineering Lakehouses with Open Table Formats
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Engineering Lakehouses with Open Table Formats

Engineering Lakehouses with Open Table Formats

By : Dipankar Mazumdar, Vinoth Govindarajan
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Engineering Lakehouses with Open Table Formats

Engineering Lakehouses with Open Table Formats

By: Dipankar Mazumdar, Vinoth Govindarajan

Overview of this book

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. You’ll explore the internals of a table format and learn in detail about the transactional capabilities of lakehouses. You’ll also get hands on with each table format with exercises using popular computing engines, such as Apache Spark, Flink, Trino, 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 of this book, 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.
Table of Contents (15 chapters)
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13
Other Books You May Enjoy
14
Index

Apache Hudi Deep Dive

Apache Hudi is an open source data lake framework that adds transactional capabilities and data management features to data lakes. This architecture is designed to handle large-scale data ingestion and processing while enabling users to perform updates, deletions, and incremental data processing pulls efficiently. At the heart of Hudi’s architecture is its integration with various storage and compute engines, such as Apache Spark, Flink, and Presto. The architecture supports both Copy-on-Write (CoW) and Merge-on-Read (MoR) table types, which provide flexibility in balancing query performance and write efficiency.

In this chapter, we will cover the following topics:

  • Hudi’s architecture and metadata layer, including file slices and catalog integration with Hive and AWS Glue
  • Core functionalities such as row-level updates, deletes, schema evolution, and real-time ingestion using Hudi Streamer
  • Practical exercises with Spark...
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