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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

Query optimization techniques

As data lakehouses continue to grow in scale and complexity, the need for efficient query performance becomes increasingly important. Query optimization techniques play a pivotal role in reducing data I/O, improving scan efficiency, and accelerating data retrieval. In modern data lakehouses, leveraging advanced metadata, indexing strategies, vectorized execution, cost-based optimization (CBO), intelligent caching, and unified materialized views has become essential for achieving high performance. This approach is particularly effective for time-series workloads, where efficient data pruning can lead to orders of magnitude performance improvements.

Some of the examples in this section reference public talks or engineering blog posts from companies such as Netflix, Microsoft, Uber, Pinterest, and Databricks. These are intended to illustrate how these techniques have been applied in practice rather than to prescribe specific performance expectations...

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