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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
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Concepts
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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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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

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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