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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

Examining Flight SQL (redux)

Way back in Chapter 8, Exploring Apache Arrow Flight RPC, we briefly touched on the topic of Arrow Flight SQL and why it was important. Very briefly. Flight SQL is still very new, and while the protocol has stabilized (for the most part), it's very much under development and there are only C++ and Java reference implementations so far. So, first, let's quickly cover the motivations for Flight SQL's development and what it is and isn't.

Why Flight SQL?

We first mentioned the Java Database Connectivity (JDBC) and Open Database Connectivity (ODBC) standards in Chapter 3, Data Science with Apache Arrow. While they have done well for decades, the standards simply don't handle columnar databases well at all. Both of these standards define APIs that are row-based. If the connected database uses a columnar representation of the data, using ODBC/JDBC will require transposing the data not once, but twice! Once for the database to provide...

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