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

Acero: A Streaming Arrow Execution Engine

We’re almost halfway through this book and only now are we covering 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 has built an open source reference implementation of a computation and query engine built on the Arrow format named Acero. To this end, the Acero library exists to facilitate various high-performance implementations of functions that operate on Arrow-formatted data, along with building execution plans for streams of 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...

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