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

Interactive charting powered by Arrow

Perspective was originally developed at J.P. Morgan and was open sourced through the Fintech Open Source Foundation (FINOS). The goal of this project was to make it easy to build analytics entirely in the browser that were user-configurable, or by using Python and/or Jupyter to create reports, dashboards, or any other application both with static data and streaming updates. It uses Apache Arrow as its underlying memory handler with a query engine built in C++ that is then compiled both for WebAssembly (for the browser/Node.js) or as a Python extension. While I highly encourage looking into it further, we're just going to cover using the PerspectiveWidget component for a Jupyter notebook to further analyze and play with the data we were using for the Spark examples, the NYC Taxi dataset.

Before we dive in, make sure that your Jupyter notebook is either still running, or you've spun it back up, as we're going to utilize it for this...