Book Image

In-Memory Analytics with Apache Arrow

By : Matthew Topol
Book Image

In-Memory Analytics with Apache Arrow

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)
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

At this point, not only should you be fairly well acquainted with a variety of topics and concepts regarding the usage of the Apache Arrow libraries, but you should also know how to start integrating them into your daily workflows. Whether you're taking advantage of the filesystem abstractions, data format conversions, or zero-copy communication benefits, Arrow can slot into a huge number of parts of any data workflow. Make sure you understand the concepts that have been touched on so far involving the formats, communication methods, and utilities provided by the Arrow libraries before moving on. Play around with them and try out different strategies for managing your data and passing it around between tools and utilities. If you're an engineer building out distributed systems, try using the Arrow IPC format (which we will learn about in detail in Chapter 4, Format and Memory Handling) and compare that with whatever previous way you passed data around. Which is easier...