Announcing RStudio and Databricks Integration
At Databricks, we are thrilled to announce the integration of RStudio with the Databricks Unified Analytics Platform. You can try it out now...
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Accelerating R Workflows on Databricks
At Databricks we strive to make our Unified Analytics Platform the best place to run big data analytics. For big data, Apache Spark...
On-Demand Webinar and FAQ: Parallelize R Code Using 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...
Shell Oil Use Case: Parallelizing Large Simulations with Apache SparkR on Databricks
This blog post is a joint engineering effort between Shell’s Data Science Team ( Wayne W. Jones and Dennis Vallinga ) and Databricks...
Using sparklyr in Databricks
Try this notebook on Databricks with all instructions as explained in this post notebook In September 2016, RStudio announced sparklyr , a new...
SparkR Tutorial at useR 2016
AMPLab and Databricks gave a tutorial on SparkR at the useR conference. The conference was held from June 27 - June 30 at...
Approximate Algorithms in Apache Spark: HyperLogLog and Quantiles
Introduction Apache Spark is fast, but applications such as preliminary data exploration need to be even faster and are willing to sacrifice some...
Introducing R Notebooks in Databricks
Apache Spark 1.4 was released on June 11 and one of the exciting new features was SparkR . I am happy to announce...
Statistics Functionality in Apache Spark 1.1
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