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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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Toc

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

Example use cases

One significant proposed benefit of having the C Data API was to allow applications to implement the API without requiring a dependency on the Arrow libraries. Let's suppose there is an existing computational engine written in C++ that wants to add the ability to return data in the Arrow format without adding a new dependency or having to link with the Arrow libraries. There are many possible reasons why you might want to avoid adding a new dependency to a project. This could range from the development environment to the complexity of deployment mechanisms, but we're not going to focus on that side of it.

Using the C Data API to export Arrow-formatted data

Do you have your development environment all set up for C++? If not, go and do that and come back. You know the drill; I'll wait.

We'll start with a small function to generate a vector of random 32-bit integers, which will act as our sample data. You know how to do that? Well, good....

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