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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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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

Swimming in data with Dremio Sonar

The roots of Arrow can be found in the ValueVector objects from the Apache Drill project, a SQL query engine for Hadoop, NoSQL, and cloud storage. Dremio Sonar was originally built out of Apache Drill and Dremio's founders co-created Arrow. Arrow is used by Dremio Sonar as the internal memory representation for its query and calculation engine, which helps power its performance. Since its inception, Dremio's engineers have made many contributions to the Arrow project resulting in significant innovations. First, let's look at the architecture used and where Arrow fits in.

Clarifying Dremio Sonar's architecture

As a distributed query engine, Dremio Sonar can be deployed in many different environments and scenarios. However, at its core, it has a pretty simple architecture, as shown in Figure 9.1. Being distributed, it can scale horizontally by increasing the number of Coordinators and Executors that handle the planning and...

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