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

Chapter 8: Exploring Apache Arrow Flight RPC

Distributed systems have always interested me. A distributed system is like a really good puzzle – immensely satisfying once you figure out how all the pieces fit together to achieve your goal. If you're not familiar with the term, a distributed system is simply a situation where you have various components of a system spread across multiple machines on a network. The idea is to split up the work and coordinate efforts among the components to complete tasks more efficiently. A great example would be Apache Spark, which we covered back in Chapter 3, Data Science with Apache Arrow.

The goal of distributed systems is generally to provide a robust, scalable, and reliable conglomeration of components that efficiently perform operations by distributing work across a system. This often means large amounts of data flowing between various components so that the data can get processed, manipulated or otherwise operated on. When it...