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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 7: Using the Arrow Datasets API

In the current ecosystem of data lakes and lakehouses, many datasets are now huge collections of files in partitioned directory structures rather than a single file. To facilitate this workflow, the Arrow libraries provide an API for easily interacting with these types of structured and unstructured data. This is called the Datasets API and is designed to perform a lot of the heavy lifting for querying these types of datasets for you.

The Datasets API provides a series of utilities for easily interacting with large, distributed, and possibly partitioned datasets that are spread across multiple files. It also integrates very easily with the Compute APIs we covered previously, in Chapter 6, Leveraging the Arrow Compute APIs.

In this chapter, we will learn how to use the Arrow Datasets API for efficient querying of multifile, tabular datasets regardless of their location or format. We will also understand how to use the dataset classes and...

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