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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

Summary

The goal of this chapter was to explain what Apache Arrow is, get you acquainted with the format, and have you use it in some simple use cases. This knowledge forms the baseline of everything else for us to talk about in the rest of the book!

Just as a reminder, you can check the GitHub repository (https://github.com/PacktPublishing/In-Memory-Analytics-with-Apache-Arrow-) for the solutions to the exercises presented here and for the full code samples to make sure you understand the concepts!

The previous examples and exercises are all fairly trivial and are meant to help reinforce the concepts introduced about the format and the specification while helping you get familiar with using Arrow in code.

In Chapter 2, Working with Key Arrow Specifications, we will introduce how to read your data into the Arrow format, whether it's on your local disk, Hadoop Distributed File System (HDFS), S3, or elsewhere, and integrate Arrow into some of the various processes and utilities you might already use with your data, such as the pandas integration. We will also discover how to pass your data around between services and processes while keeping it in the Arrow format for performance.

Ready? Onward and upward!

CONTINUE READING
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