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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

Product type Paperback

Published in Sep 2024

Publisher Packt

ISBN-13 9781835461228

Length 406 pages

Edition 2nd Edition

Languages

Python

Tools

Apache arrow

Concepts

Data Engineering

Author (1):

Matthew Topol

View More author details

Table of Contents (18) Chapters

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

Why subscribe?

17. Other Books You May Enjoy

Technical requirements

Once again, we’ll have lots of technical examples in this chapter. So, you’re going to need the following:

An internet-connected computer.
A single or multi-node Apache Spark cluster also running Jupyter. Docker is the easiest way to set it up. We’ll cover doing that in this chapter.
Python 3.8+ with the pyarrow module and the dataset submodule installed.
Your preferred coding IDE, such as Emacs, Vim, Sublime, or VS Code.

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Authors (1)

Matthew Topol

Matthew Topol

Matthew Topol is a member of the Apache Arrow Project Management Committee (PMC) and a staff software engineer at Voltron Data, Inc. Matt has worked in infrastructure, application development, and large-scale distributed system analytical processing for financial data. At Voltron Data, Matt's primary responsibilities have been working on and enhancing the Apache Arrow libraries and associated sub-projects. In his spare time, Matt likes to bash his head against a keyboard, develop and run delightfully demented fantasy games for his victims—er—friends, and share his knowledge and experience with anyone interested enough to listen.

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