Large data processing with SQL for social science research Session II: Continuing SQL and project workflows in R

Monday, November 11, 4:30pm - 6:00pm (EST)

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This series introduces SQL for social scientists, with a focus on DuckDB -- a portable, fast, and scalable database for analytics. With only a few basics, researchers can easily scale projects to handle most larger-than-memory data processing tasks without need of a cluster. We will discuss how to conveniently integrate DuckDB's SQL dialect into existing R (and Python) workflows. We conclude with special topics, which may include geospatial data processing, databases on the cluster, and some SQL best practices.

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Add to Calendar 2024/11/11 16:30:00 2024/11/11 18:00:00 America/New_York Large data processing with SQL for social science research Session II: Continuing SQL and project workflows in R

This series introduces SQL for social scientists, with a focus on DuckDB -- a portable, fast, and scalable database for analytics. With only a few basics, researchers can easily scale projects to handle most larger-than-memory data processing tasks without need of a cluster. We will discuss how to conveniently integrate DuckDB's SQL dialect into existing R (and Python) workflows. We conclude with special topics, which may include geospatial data processing, databases on the cluster, and some SQL best practices.

Bendheim House, Room 103 Initiative for Data-Driven Social Science ddss@princeton.edu false 180

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A confirmation email is on its way. Check your spam folder if it doesn't show up.

Add to Calendar 2024/11/11 16:30:00 2024/11/11 18:00:00 America/New_York Large data processing with SQL for social science research Session II: Continuing SQL and project workflows in R

This series introduces SQL for social scientists, with a focus on DuckDB -- a portable, fast, and scalable database for analytics. With only a few basics, researchers can easily scale projects to handle most larger-than-memory data processing tasks without need of a cluster. We will discuss how to conveniently integrate DuckDB's SQL dialect into existing R (and Python) workflows. We conclude with special topics, which may include geospatial data processing, databases on the cluster, and some SQL best practices.

Bendheim House, Room 103 Initiative for Data-Driven Social Science ddss@princeton.edu false 180

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Bendheim House, Room 103

Initiative for Data-Driven Social Science, ddss@princeton.edu