9781032660288 - Scaling Up with R and Apache Arrow: Bigger Data, Easier Workflows de Crane, Nic; Keane, Jonathan; Richardson, Neal (15 resultados)

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Taschenbuch. Condición: Neu. Scaling Up with R and Apache Arrow | Bigger Data, Easier Workflows | Nic Crane (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | Chapman and Hall/CRC | EAN 9781032660288 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu.

Paperback. Condición: new. Paperback. 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. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 162 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Nic Crane is an R developer, educator, and general enthusiast, with a background in data science and software engineering. Nic is a member of the Apache Arrow Project Management Committee (PMC) and part of the team who maintains the arrow R packag.

Paperback. Condición: new. Paperback. 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.