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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

Chapter 2: Working with Key Arrow Specifications

Utilities to perform analytics and computations are only useful if you have data to perform them on. That data can live in many different places and formats, both local and remote to the machine being used to analyze it. The Arrow libraries provide a bunch of functionalities that we'll cover for reading data from and interacting with multiple different formats in multiple different locations. Now that you have a solid understanding of what Arrow is and how to manipulate arrays, in this chapter, you will learn how to get data into the Arrow format and communicate it between different processes.

In this chapter, we're going to cover the following topics:

  • Importing data from multiple formats, including CSV, Apache Parquet, and pandas DataFrames
  • Interactions between Arrow and pandas data
  • Utilizing shared memory for near zero-cost data sharing

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