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In-Memory Analytics with Apache Arrow

You're reading from   In-Memory Analytics with Apache Arrow Perform fast and efficient data analytics on both flat and hierarchical structured data

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Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781801071031
Length 392 pages
Edition 1st Edition
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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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Table of Contents (16) Chapters Close

Preface 1. Section 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: Data Science with Apache Arrow 5. Section 2: Interoperability with Arrow: pandas, Parquet, Flight, and Datasets
6. Chapter 4: Format and Memory Handling 7. Chapter 5: Crossing the Language Barrier with the Arrow C Data API 8. Chapter 6: Leveraging the Arrow Compute APIs 9. Chapter 7: Using the Arrow Datasets API 10. Chapter 8: Exploring Apache Arrow Flight RPC 11. Section 3: Real-World Examples, Use Cases, and Future Development
12. Chapter 9: Powered by Apache Arrow 13. Chapter 10: How to Leave Your Mark on Arrow 14. Chapter 11: Future Development and Plans 15. Other Books You May Enjoy

Technical requirements

Throughout this chapter, I'll be providing various code samples while using the Python, C++, and Golang Arrow libraries and the public NYC Taxi Trip Duration dataset.

To run the practical examples in this chapter, you will need the following:

  • A computer that's connected to the internet
  • Your preferred IDE (VS Code, Sublime, Emacs, Vim, and so on)
  • Python 3+ with the pyarrow and pandas modules installed
  • Go 1.16+
  • A C++ compiler (capable of compiling C++11 or newer) with the Arrow libraries installed
  • The sample_data folder from this book's GitHub repository: https://github.com/PacktPublishing/In-Memory-Analytics-with-Apache-Arrow-

Let's get started!

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