Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying In-Memory Analytics with Apache Arrow
  • Table Of Contents Toc
In-Memory Analytics with Apache Arrow

In-Memory Analytics with Apache Arrow

By : Matthew Topol
4.9 (15)
close
close
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)
close
close
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...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
In-Memory Analytics with Apache Arrow
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon