Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
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 Perform fast and efficient data analytics on both flat and hierarchical structured data

Arrow left icon
Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781801071031
Length 392 pages
Edition 1st Edition
Languages
Concepts
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 (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

Chapter 6: Leveraging the Arrow Compute APIs

We're halfway through this book and only now are we covering actually performing analytical computations directly with Arrow. Kinda strange, right? At this point, if you've been following along, you should have a solid understanding of all the concepts you'll need to be able to benefit from the compute library.

The Arrow community is working toward building open source computation and query engines built on the Arrow format. To this end, the Arrow compute library exists to facilitate various high-performance implementations of functions that operate on Arrow-formatted data. This might be to perform logical casting from one data type to another, or it might be for performing large computation and filter operations, and everything in between. Rather than consumers having to implement operations over and over, high-performance implementations can be based on the Arrow format in a generic fashion and then used by many consumers...

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
Banner background image