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

Final words

This brings us to the end of this journey. I've tried to pack lots of useful information, tips, tricks, and diagrams into this book, but there's also plenty of room for much more research and experimentation on your end! If you haven't done so already, go back and try the various exercises I've proposed in the chapters. Explore new things with the Arrow datasets and compute APIs, and try using Arrow Flight in your own work.

Across the various chapters in this book, we've covered a lot of stuff, such as the following:

  • The Arrow format specification
  • Using the Arrow libraries to improve many aspects of analytical computation and data science
  • Inter-process communication and sharing memory
  • Using Apache Spark, pandas, and Jupyter in conjunction with Arrow
  • Utilizing existing tools for interactive visualizations
  • The differences between data storage formats and in-memory runtime formats
  • Passing data across the boundaries...