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In-Memory Analytics with Apache Arrow

In-Memory Analytics with Apache Arrow

By : Matthew Topol
4.9 (15)
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In-Memory Analytics with Apache Arrow

In-Memory Analytics with Apache Arrow

4.9 (15)
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)
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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

Other use cases

In addition to providing an interface for zero-copy sharing of Arrow data between components, the C Data API can also be used in cases where it may not be feasible to depend on the Arrow libraries directly.

Despite a large number of languages and runtimes sporting implementations of Arrow, there are still languages or environments that do not have Arrow implementations. This is particularly true in organizations with a lot of legacy software and/or specialized environments. A great example of this would be the fact that the dominant programming language in the astrophysical modeling of stars and galaxies is still Fortran! Unsurprisingly, there is not an existing Arrow implementation for Fortran. In these situations, it is often not feasible to rewrite entire code bases so that you can leverage Arrow in a supported language. But with the C Data API, data can be shared from a supported runtime to a pre-existing unsupported code base. Alternatively, you can do the...

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