At Databricks we strive to make our Unified Analytics Platform the best place to run big data analytics. For big data, Apache Spark...

On August 15th, Data Science Central hosted a live webinar—Parallelize R Code Using Apache Spark—with Databricks’ Hossein Falaki . This webinar introduced SparkR...

This blog post is a joint engineering effort between Shell’s Data Science Team ( Wayne W. Jones and Dennis Vallinga ) and Databricks...

Try this notebook on Databricks with all instructions as explained in this post notebook In September 2016, RStudio announced sparklyr , a new...

AMPLab and Databricks gave a tutorial on SparkR at the useR conference. The conference was held from June 27 - June 30 at...

Introduction Apache Spark is fast, but applications such as preliminary data exploration need to be even faster and are willing to sacrifice some...

Apache Spark 1.4 was released on June 11 and one of the exciting new features was SparkR . I am happy to announce...

One of our philosophies in Apache Spark is to provide rich and friendly built-in libraries so that users can easily assemble data pipelines. With Spark, and MLlib in particular, quickly gaining traction among data scientists and machine learning practitioners, we’re observing a growing demand for data analysis support outside of model fitting. To address this need, we have started to add scalable implementations of common statistical functions to facilitate v