Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781835461228
Length 406 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Matthew Topol Matthew Topol
Author Profile Icon Matthew Topol
Matthew Topol
Arrow right icon
View More author details
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

Using the Arrow C data interface

Back in Chapter 2, Working with Key Arrow Specifications, I mentioned the Arrow C data interfaces regarding the communication of data between Python and Spark processes. At that point, we didn’t go much into detail about the interface or what it looks like; now, we will.

Since the Arrow project is fast-moving and constantly evolving, it can sometimes be difficult for other projects to incorporate the Arrow libraries into their work. There’s also the case where there might be a lot of existing code that needs to be adapted to work with Arrow piecemeal, leading to you having to create or even re-implement adapters for interchanging data. To avoid redundant efforts across these situations, the Arrow project defines a small, stable set of C definitions that can be copied into a project to allow easily passing data across the boundaries of different languages and libraries. For languages and runtimes that aren’t C or C++, it should...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime