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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 FREE CHAPTER
2. Chapter 1: Getting Started with Apache Arrow 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

Format and Memory Handling

I’ve continuously extolled the virtues of using Arrow as a data interchange technology for tabular data, but how does it stack up against the more common technologies that people tend to utilize for transferring data? When does it make sense to use one over the other for your application programming interface (API)? To answer these questions, you need to know how different technologies utilize memory. Clever management of your memory can be the key to performant processes. To decide which format to use for your data, you need to understand which use cases your options were designed for. With that in mind, you can take advantage of the runtime properties of the most common data transport formats, such as Protocol Buffers (Protobuf), JavaScript Object Notation (JSON), and FlatBuffers, when appropriate. By understanding how to utilize memory in your program, you can process very large amounts of data with minimal memory overhead. You can even shift data...

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