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

The compute APIs aren't just a convenient interface for performing functions on Arrow-formatted data but are also highly performant. The goal of the library is to expose highly optimized computational functions for as many use cases as possible in an easy-to-use way. The functions that it exposes are also highly composable as we saw with the examples for sorting a table.

Between this chapter and the previous one, Chapter 5, Crossing the Language Barrier with the Arrow C Data API, we've explored the building blocks of any analytical engine. Both the Arrow C data interface and the compute APIs are extremely useful in different use cases and even in conjunction with one another. For example, let's say you're using Arrow in a language that doesn't yet expose the compute APIs. By using the C Data API, you can efficiently share the data with another component that has access to the compute APIs.

Now, if you're dealing with multifile datasets of...