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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
2. Chapter 1: Getting Started with Apache Arrow FREE CHAPTER 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

Summary

If you need to transport data from one place to another, you should consider Arrow Flight as a possible solution for your server. Even without taking advantage of the parallelism it provides, it still shows that it is a significant improvement over most current ways to transport tabular data. Because it leverages Arrow’s IPC format, it gains the benefits of avoiding serialization and deserialization costs. While it is currently built on gRPC using HTTP/2, there are many proposals to enable it to more easily support other networking configurations that may get traction.

During the course of this chapter, if you followed along, you created some simple Flight servers and clients. Let those serve as boilerplates for you. Build on them. Add more functionality to them. Explore the features and abilities that Flight provides and supports. If you work more on the data science side than on the developer side, try using Flight as a client to fetch data from engines such as...

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