Javier Luraschi

Javier is the author of “Mastering Spark with R”, pins, sparklyr, mlflow and torch. He holds a double degree in Math and Software Engineer and decades of industry experience with a focus on data analysis. Javier is currently working on a project of his own; and previously worked in RStudio, Microsoft Research and SAP.


blog

pins 0.4.0: Versioning

August 23, 2021

A new release of pins is available on CRAN today. This release adds support to time travel across dataset versions, which improves collaboration and protects your code from breaking when remote resources change unexpectedly.

rstudio::global 2021

Using pins with Python and JavaScript

January 21, 2021

Last year, pins got released as a brand new R package to pin, discover and cache remote resources for R users.

rstudio::conf 2020

Datasets in Reproducible Research with 'pins'

February 4, 2020

Open source code is an essential piece in making science reproducible. Tools like 'rmarkdown' and GitHub facilitate running and sharing outcomes with colleagues and with the broad scientific community

blog

pins 0.3.0: Azure, GCloud and S3

November 28, 2019

New CRAN release for pins adds support for cloud boards (Azure, Google Cloud and S3), new functions, use cases and many other improvements.

rstudio::conf 2019

Scaling R with Spark

January 25, 2019

This talk introduces new features in sparklyr that enable real-time data processing, brand new modeling extensions and significant performance improvements.

Webinars

Part 3 - Advanced features of sparklyr

August 23, 2017

sparklyr facilitates a connection between R and Spark using a full-fledged dplyr backend with support for the entirety of Spark’s MLlib library.

Webinars

Part 2 - Extending Spark using sparklyr

August 17, 2017

sparklyr facilitates a connection between R and Spark using a full-fledged dplyr backend with support for the entirety of Spark’s MLlib library.