Shiny and Environments for R Notebooks
At Databricks, we want the Lakehouse ecosystem widely accessible to all data practitioners, and R is a great interface language for this purpose because of its rich ecosystem of open source packages and broad use as a computing language for many non-computing scientific disciplines. The product team at Databricks actively engages with R users to...
Introducing the Databricks Web Terminal
Introduction We're excited to introduce the public preview of the Databricks Web Terminal in the 3.25 platform release. Any user with "Can Attach To" cluster permissions can now use the Web Terminal to interactively run Bash commands on the driver node of their cluster. The new Databricks web terminal provides a fully interactive shell that...
%tensorboard – a new way to use TensorBoard on Databricks
Introduction With the Databricks Runtime 7.2 release, we are introducing a new magic command %tensorboard. This brings the interactive TensorBoard experience Jupyter notebook users expect to their Databricks notebooks. The %tensorboard command starts a TensorBoard server and embeds the TensorBoard user interface inside the Databricks notebook for data scientists and machine learning engineers to visualize...
Developing Shiny Applications in Databricks
Join our live webinar hosted by Data Science Central on March 12 to learn more We are excited to announce that you can now develop and test Shiny applications in Databricks! Inside the RStudio Server hosted on Databricks clusters, you can now import the Shiny package and interactively develop Shiny applications. Once completed, you can...
Introducing Databricks Runtime 5.4 with Conda (Beta)
We are excited to introduce a new runtime: Databricks Runtime 5.4 with Conda (Beta). This runtime uses Conda to manage Python libraries and environments. Many of our Python users prefer to manage their Python environments and libraries with Conda, which quickly is emerging as a standard. Conda takes a holistic approach to package management by...
Accelerating Machine Learning on Databricks: On-Demand Webinar and FAQ Now Available!
On January 15th, we hosted a live webinar—Accelerating Machine Learning on Databricks—with Adam Conway, VP of Product Management, Machine Learning, at Databricks and Hossein Falaki, Software Development Engineer and Data Scientist at Databricks. In this webinar, we covered some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning. In particular,...
Introducing Databricks Runtime 5.1 for Machine Learning
Last week, we released Databricks Runtime 5.1 Beta for Machine Learning. As part of our commitment to provide developers with the latest deep learning frameworks, this release includes the best of these libraries. In particular, our PyTorch addition makes it simple for a developer to simply import the appropriate Python torch modules and start coding,...
Introducing Databricks Runtime 5.0 for Machine Learning
Six months ago we introduced the Databricks Runtime for Machine Learning with the goal of making machine learning performant and easy on the Databricks Unified Analytics Platform. The Databricks Runtime for ML comes pre-packaged with many ML frameworks and enables distributed training and inference. Today we are excited to release the second iteration including Conda...
100x Faster Bridge between Apache Spark and R with User-Defined Functions on Databricks
SparkR User-Defined Function (UDF) API opens up opportunities for big data workloads running on Apache Spark to embrace R's rich package ecosystem. Some of our customers that have R experts on board use SparkR UDF API to blend R's sophisticated packages into their ETL pipeline, applying transformations that go beyond Spark’s built-in functions on the...
Sharing R Notebooks using RMarkdown
At Databricks, we are thrilled to announce the integration of RStudio with the Databricks Unified Analytics Platform. You can try it out now with this RMarkdown notebook (Rmd | HTML) or visit us at www.databricks.com/rstudio. Introduction Databricks Unified Analytics Platform now supports RStudio Server (press release). Users often ask if they can move notebooks between RStudio and Databricks workspace using...
