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

Getting that sweet, sweet approval

So, you found the perfect issue to work on, made a PR, and got the CI tests to succeed. What next? How do you get your contribution to be accepted and merged?

Well, core Apache Arrow developers and possibly others who have a stake in the particular area of the Arrow libraries you're modifying will review your PR. To ensure a good review, keep the following in mind:

  • Aside from passing existing unit tests, it's expected that new functionality also adds new unit tests to ensure that it is properly tested.
  • As much as possible, break your work into smaller single-purpose patches. It's significantly more difficult to get a larger change with disjointed features merged.
  • Follow the style guide for the library you're modifying (more on that in the Finishing up with style! section next).

Once any feedback has been addressed and the PR is approved, one of the committers will merge your PR. Congratulations! You&apos...