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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

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Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781801071031
Length 392 pages
Edition 1st 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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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

Preface

To quote a famous blue hedgehog, Gotta Go Fast! When it comes to data, speed is important. It doesn't matter if you're collecting or analyzing data or developing utilities for others to do so, performance and efficiency are going to be huge factors in your technology choices, not just in the efficiency of the software itself, but also in development time. You need the right tools and the right technology, or you're dead in the water.

The Apache Arrow ecosystem is developer-centric, and this book is no different. Get started with understanding what Arrow is and how it works, then learn how to utilize it in your projects. You'll find code examples, explanations, and diagrams here, all with the express purpose of helping you learn. You'll integrate your data sources with Python DataFrame libraries such as pandas or NumPy and utilize Arrow Flight to create efficient data services.

With real-world datasets, you'll learn how to leverage Apache Arrow with Apache Spark and other technologies. Apache Arrow's format is language-independent and organized so that analytical operations are performed extremely quickly on modern CPU and GPU hardware. Join the industry adoption of this open source data format and save yourself valuable development time creating high-performant, memory-efficient, analytical workflows.

This book has been a labor of love to share knowledge. I hope you learn a lot from it! I sure did when writing it.

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