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In-Memory Analytics with Apache Arrow

You're reading from   In-Memory Analytics with Apache Arrow Accelerate data analytics for efficient processing of flat and hierarchical data structures

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781835461228
Length 406 pages
Edition 2nd Edition
Languages
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Author (1):
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Matthew Topol Matthew Topol
Author Profile Icon Matthew Topol
Matthew Topol
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Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1: Overview of What Arrow is, Its Capabilities, Benefits, and Goals FREE CHAPTER
2. Chapter 1: Getting Started with Apache Arrow 3. Chapter 2: Working with Key Arrow Specifications 4. Chapter 3: Format and Memory Handling 5. Part 2: Interoperability with Arrow: The Power of Open Standards
6. Chapter 4: Crossing the Language Barrier with the Arrow C Data API 7. Chapter 5: Acero: A Streaming Arrow Execution Engine 8. Chapter 6: Using the Arrow Datasets API 9. Chapter 7: Exploring Apache Arrow Flight RPC 10. Chapter 8: Understanding Arrow Database Connectivity (ADBC) 11. Chapter 9: Using Arrow with Machine Learning Workflows 12. Part 3: Real-World Examples, Use Cases, and Future Development
13. Chapter 10: Powered by Apache Arrow 14. Chapter 11: How to Leave Your Mark on Arrow 15. Chapter 12: Future Development and Plans 16. Index 17. Other Books You May Enjoy

Final words

This brings us to the end of this journey. I’ve tried to pack lots of useful information, tips, tricks, and diagrams into this book, but there’s also plenty of room for much more research and experimentation on your end! If you haven’t done so already, go back and try the various exercises I’ve proposed throughout. Explore new things with the Arrow datasets and compute APIs, and try using Arrow Flight and ADBC in your work.

Across the various chapters in this book, we’ve covered a lot of things:

  • The Arrow format specification
  • Using the various Arrow libraries to improve many aspects of analytical computation and data science
  • Inter-process communication and sharing memory
  • Using Apache Spark, pandas, and Jupyter in conjunction with Arrow
  • The differences between data storage formats and in-memory runtime formats
  • Passing data across the boundaries of programming languages without having to copy it
  • Using gRPC...
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