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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

Stretching workflows onto Elasticsearch

If what you need is primarily searching and filtering large amounts of data rather than heavy analytical computations, chances are you've probably looked into Elasticsearch. Even if you do need heavy computations, you might be able to pre-calculate large amounts of data and store it in Elasticsearch to fetch later to speed up your queries. However, there's a slight issue: Elasticsearch's API is entirely built in JSON, and Arrow is a binary format. We also don't want to sacrifice our fast data transportation using Arrow's IPC format if we can avoid it!

I recently worked on a project where the solution we came up with was to have a unified service interface that used Arrow, but heuristically determine when a request would be better serviced by an Elasticsearch query and simply convert the data from the JSON returned by Elasticsearch to Arrow. If this seems overly complicated, here's what this solution achieved for...

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