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In-Memory Analytics with Apache Arrow

You're reading from   In-Memory Analytics with Apache Arrow Accelerate data analytics for efficient processing of flat and hierarchical data structures

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781835461228
Length 406 pages
Edition 2nd 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 (18) Chapters Close

Preface 1. Part 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: Format and Memory Handling 5. Part 2: Interoperability with Arrow: The Power of Open Standards
6. Chapter 4: Crossing the Language Barrier with the Arrow C Data API 7. Chapter 5: Acero: A Streaming Arrow Execution Engine 8. Chapter 6: Using the Arrow Datasets API 9. Chapter 7: Exploring Apache Arrow Flight RPC 10. Chapter 8: Understanding Arrow Database Connectivity (ADBC) 11. Chapter 9: Using Arrow with Machine Learning Workflows 12. Part 3: Real-World Examples, Use Cases, and Future Development
13. Chapter 10: Powered by Apache Arrow 14. Chapter 11: How to Leave Your Mark on Arrow 15. Chapter 12: Future Development and Plans 16. Index 17. Other Books You May Enjoy

Why does Arrow use a columnar in-memory format?

There is often a lot of debate surrounding whether a database should be row-oriented or column-oriented, but this primarily refers to the on-disk format of the underlying storage files. Arrow’s data format is different from most cases discussed so far since it uses a columnar organization of data structures in memory directly. If you’re not familiar with columnar as a term, let’s take a look at what it means. First, imagine the following table of data:

Figure 1.3 – Sample data table

Figure 1.3 – Sample data table

Traditionally, if you were to read this table into memory, you’d likely have some structure to represent a row and then read the data in one row at a time – maybe something like struct { string archer; string location; int year }. The result is that you have the memory grouped closely together for each row, which is great if you always want to read all the columns for every row or are...

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In-Memory Analytics with Apache Arrow - Second Edition
Published in: Sep 2024
Publisher: Packt
ISBN-13: 9781835461228
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