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

Firing a Ballista using Data(Fusion)

Started as a personal project, the distributed compute platform called Ballista was donated to the Arrow project. Ballista is implemented in Rust and powered by Arrow as its internal memory model. Underneath Ballista's scheduling and coordination infrastructure is Arrow DataFusion, a query planning and execution framework. What does all this mean? Well, I'm glad you asked!

Most large data computation is done using some sort of distributed cluster. Multiple machines work together in a coordinated fashion to complete complex tasks. A great example of a framework like this you might be familiar with is Apache Spark. Currently, the architecture of Ballista looks something like Figure 11.4. You'll note the usage of Arrow Flight as the communication protocol along with a client for Rust and Python:

Figure 11.4 – Ballista cluster architecture (today)

The end goal of the project is to eventually have an...