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

To get the most out of this book

It is assumed that you have a basic understanding of writing code in at least one of C++, Python, or Go to benefit from and use the code snippets. You should know how to compile and run code in the desired language. Some familiarity with the basic concepts of data analysis will help you to get the most out of this book. Beyond this, concepts such as tabular data and installing software on your machine are assumed to be understood.

The sample data is in the book's GitHub repository. You'll need to use Git Large File Storage (LFS) or a browser to download the large data files. There are also a couple of large sample data files in a publicly accessible AWS S3 bucket. The book will provide a link to download the files when necessary. Code examples are provided in C++, Python, and Go.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book's GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Take your time, enjoy, and experiment in all kinds of ways, and please, have fun with the exercises.