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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

Questions

  1. What are the four ACID properties, and why are they crucial for transactional systems?
  2. Explain the differences between pessimistic concurrency control and optimistic concurrency control.
  3. What is multi-version concurrency control (MVCC), and how does it manage conflicts?
  4. Describe the role of the lock manager in the storage engine of a lakehouse architecture.
  5. How do access methods in a storage engine facilitate efficient data retrieval?
  6. What is the significance of compaction in table management services?
  7. Explain how clustering improves query performance in lakehouse architectures.
  8. What challenges do traditional data lakes face in ensuring ACID compliance? How do lakehouses address these challenges?
  9. How does the recovery manager ensure data durability and consistency in case of system failures?
  10. List two cleaning mechanisms used in Apache Hudi, Apache Iceberg, or Delta Lake, and describe their purpose.
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83
Tech Concepts
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Programming languages
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Engineering Lakehouses with Open Table Formats
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