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In-Memory Analytics with Apache Arrow

You're reading from   In-Memory Analytics with Apache Arrow Perform fast and efficient data analytics on both flat and hierarchical structured data

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Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781801071031
Length 392 pages
Edition 1st Edition
Languages
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Author (1):
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Matthew Topol Matthew Topol
Author Profile Icon Matthew Topol
Matthew Topol
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Overview of What Arrow Is, its Capabilities, Benefits, and Goals
2. Chapter 1: Getting Started with Apache Arrow FREE CHAPTER 3. Chapter 2: Working with Key Arrow Specifications 4. Chapter 3: Data Science with Apache Arrow 5. Section 2: Interoperability with Arrow: pandas, Parquet, Flight, and Datasets
6. Chapter 4: Format and Memory Handling 7. Chapter 5: Crossing the Language Barrier with the Arrow C Data API 8. Chapter 6: Leveraging the Arrow Compute APIs 9. Chapter 7: Using the Arrow Datasets API 10. Chapter 8: Exploring Apache Arrow Flight RPC 11. Section 3: Real-World Examples, Use Cases, and Future Development
12. Chapter 9: Powered by Apache Arrow 13. Chapter 10: How to Leave Your Mark on Arrow 14. Chapter 11: Future Development and Plans 15. Other Books You May Enjoy

Chapter 8: Exploring Apache Arrow Flight RPC

Distributed systems have always interested me. A distributed system is like a really good puzzle – immensely satisfying once you figure out how all the pieces fit together to achieve your goal. If you're not familiar with the term, a distributed system is simply a situation where you have various components of a system spread across multiple machines on a network. The idea is to split up the work and coordinate efforts among the components to complete tasks more efficiently. A great example would be Apache Spark, which we covered back in Chapter 3, Data Science with Apache Arrow.

The goal of distributed systems is generally to provide a robust, scalable, and reliable conglomeration of components that efficiently perform operations by distributing work across a system. This often means large amounts of data flowing between various components so that the data can get processed, manipulated or otherwise operated on. When it...

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