Scaling Up with R and Apache Arrow - Crane, Nic; Keane, Jonathan; Richardson, Neal; - Prospero Internet Bookshop
Basket (0)
Wishlist (0)
Your personal newsletter
__

Scaling Up with R and Apache Arrow: Bigger Data, Easier Workflows
 
Product details:
ISBN13:9781032663203
ISBN10:10326632011
Binding:Hardback
No. of pages:168 pages
Size:234x156 mm
Weight:453 g
Language:English
Illustrations: 20 Illustrations, black & white; 20 Line drawings, black & white; 12 Tables, black & white
700
Category:

Scaling Up with R and Apache Arrow

Bigger Data, Easier Workflows
 
Edition number: 1
Publisher: Chapman and Hall
Date of Publication:
 
Normal price:
Publisher's listprice:
GBP 120.00
Estimated price in HUF:
60 732 HUF (57 840 HUF + 5% VAT)
Why estimated?
 
Your price:
54 659 (52 056 HUF + 5% VAT )
discount is: 10% (approx 6 073 HUF off)
The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
Click here to subscribe.
 
Availability:
Not yet published.
 
Add to basket
  Piece(s)

 
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.

Long 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.

Table 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