Scaling Up with R and Apache Arrow
Bigger Data, Easier Workflows
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60 732 Ft
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
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Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 1 June 2025
- ISBN 9781032663203
- Binding Hardback
- No. of pages160 pages
- Size 254x178 mm
- Weight 460 g
- Language English
- Illustrations 20 Illustrations, black & white; 20 Line drawings, black & white; 12 Tables, black & white 700
Categories
Short description:
This book provides a guide to working efficiently with larger-than-memory datasets using the arrow R package. You'll learn how to overcome these hurdles without needing to set up complex infrastructure. Written by developers of the Arrow R package, this guide is essential for anyone looking to scale their data processing capabilities in R.
MoreLong description:
Analyze large datasets directly from R. Scaling Up With R and Arrow provides a guide to working efficiently with larger-than-memory datasets using the arrow R package. As data grows in size and complexity, traditional data analysis methods in R often hit technical limitations. In this book, you'll learn how to overcome these hurdles without needing to set up complex infrastructure.
You'll learn about the Apache Arrow project's origins, goals, and its significance in bridging the gap between data science and big data ecosystems. You'll also learn how to leverage the arrow R package to work directly with files in various formats, such as CSV and Parquet, using familiar dplyr syntax. This book explores practical topics like data manipulation, file formats, working with larger datasets, and optimizing workflows for data in cloud storage. Advanced chapters examine user-defined functions, integration with other tools like DuckDB, and extending Arrow's capabilities to work with geospatial data.
Written by developers of the Arrow R package, this guide is essential for anyone looking to scale their data processing capabilities in R.
MoreTable of Contents:
Acknowledgements Foreword 1. Introduction 2. Getting Started 3. Data Manipulation 4. Files and Formats 5. Datasets 6. Cloud 7. Advanced Topics 8. Sharing Data and Interoperability References Appendices
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