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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

Summary

With Jupyter, Spark, and ODBC as some of the most ubiquitous utilities in data science, it only makes sense to cover Arrow from the perspective of its integration with these tools. Many of you will likely not use Arrow directly in these cases, but rather benefit from the work being done by others utilizing Arrow. But, if you're a library or utility builder, or just want to tinker a bit to see whether you can improve the performance of some different tasks, this chapter should have given you a lot of information to chew on and hopefully a bunch of ideas to try out, such as converting Arrow on the fly to populate an Elasticsearch index but maintain a consistent interface.

I don't want to give you all the answers, mostly because I don't have them. There's a wealth of people all over experimenting with Arrow in a large number of different use cases, some of which we'll cover in other chapters. Hopefully, this chapter, and the chapters to come after it...

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