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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

Always have a plan

Due to the columnar nature of the Arrow format, along with the separation of the validity bitmap from the data buffers, it’s easy to see why it is a popular choice for interoperability between systems. Some query engines, such as InfluxDB, have adopted Arrow as their internal format due to its efficiency when it comes to performing computations. Others, such as DuckDB and Velox, have internal representations that are nearly identical to Arrow, providing zero-copy interactions with the rest of the ecosystem. We’ll touch more on those projects in a later chapter, but for now we’re going to examine Acero, a reference implementation of an execution engine using Arrow as its internal data representation.

One thing I want to stress here is that in most cases, while the compute functions are extremely useful, Acero is not intended to be used directly by data scientists. Typically, users would use some kind of frontend (i.e., pandas, Ibis, or a SQL...

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