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In-Memory Analytics with Apache Arrow

In-Memory Analytics with Apache Arrow

By : Matthew Topol
4.9 (15)
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In-Memory Analytics with Apache Arrow

In-Memory Analytics with Apache Arrow

4.9 (15)
By: Matthew Topol

Overview of this book

Apache Arrow is designed to accelerate analytics and allow the exchange of data across big data systems easily. In-Memory Analytics with Apache Arrow begins with a quick overview of the Apache Arrow format, before moving on to helping you to understand Arrow’s versatility and benefits as you walk through a variety of real-world use cases. You'll cover key tasks such as enhancing data science workflows with Arrow, using Arrow and Apache Parquet with Apache Spark and Jupyter for better performance and hassle-free data translation, as well as working with Perspective, an open source interactive graphical and tabular analysis tool for browsers. As you advance, you'll explore the different data interchange and storage formats and become well-versed with the relationships between Arrow, Parquet, Feather, Protobuf, Flatbuffers, JSON, and CSV. In addition to understanding the basic structure of the Arrow Flight and Flight SQL protocols, you'll learn about Dremio’s usage of Apache Arrow to enhance SQL analytics and discover how Arrow can be used in web-based browser apps. Finally, you'll get to grips with the upcoming features of Arrow to help you stay ahead of the curve. By the end of this book, you will have all the building blocks to create useful, efficient, and powerful analytical services and utilities with Apache Arrow.
Table of Contents (16 chapters)
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1
Section 1: Overview of What Arrow Is, its Capabilities, Benefits, and Goals
5
Section 2: Interoperability with Arrow: pandas, Parquet, Flight, and Datasets
11
Section 3: Real-World Examples, Use Cases, and Future Development

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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In-Memory Analytics with Apache Arrow
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