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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

Product type Paperback

Published in Jun 2022

Publisher Packt

ISBN-13 9781801071031

Length 392 pages

Edition 1st Edition

Languages

C

Tools

Apache arrow

Concepts

Big Data

Author (1):

Matthew Topol

View More author details

Table of Contents (16) Chapters

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 2: Working with Key Arrow Specifications

Utilities to perform analytics and computations are only useful if you have data to perform them on. That data can live in many different places and formats, both local and remote to the machine being used to analyze it. The Arrow libraries provide a bunch of functionalities that we'll cover for reading data from and interacting with multiple different formats in multiple different locations. Now that you have a solid understanding of what Arrow is and how to manipulate arrays, in this chapter, you will learn how to get data into the Arrow format and communicate it between different processes.

In this chapter, we're going to cover the following topics:

Importing data from multiple formats, including CSV, Apache Parquet, and pandas DataFrames
Interactions between Arrow and pandas data
Utilizing shared memory for near zero-cost data sharing

The rest of the chapter is locked

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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.

See other products by Matthew Topol

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