R-Brain is a next generation platform for data science built on top of Jupyterlab with Docker, which supports not only R, but also Python, SQL, has integrated intellisense, debugging, packaging, and publishing capabilities.
R-Brain is a next generation platform for data science built on top of Jupyterlab with Docker. It was recently unveiled at JupyterCon in late August. Don’t let the name fool you. R-Brain currently supports R, Python, SQL, and more. It has integrated intellisense, debugging, packaging, and publishing capabilities. This cool new solution also has analytics workspace collaboration and marketplace features for personal, professional and enterprise use cases.
R-Brain applications developed by experts can speed up private group development in an enterprise or be shared in the public R-Brain marketplace. The R-Brain marketplace is ideal for teaching or sharing analytics assets for free or for a fee. Currently Golden Gate University is using R-Brain in their analytics programs.
R-Brain improves multi-language data analysis productivity. It offers all the familiar building blocks of the classic Jupyter Notebook (interactive notebook, terminal, text editor, file browser, rich outputs, etc.) in a flexible, powerful user interface. Since it uses popular Docker container technology, this solution can be deployed on-premises or on your preferred cloud platform. Data gurus can develop, package, share and publish analytics workspaces, data sets and applications that use R, Python, Structured Query Language (SQL) scripts. R-Brain also makes it easy to interactively navigate database schemas, view table content and export data.
Today most data analysts and data scientists have a toolbox filled with a mix of utilities for Python, R, SQL, and other script languages. For example, you might use RStudio for R, Jupyter(iPython notebooks), Anaconda, PyCharm, Spyder, or Apache Zeppelin for Python, a SQL IDE like TOAD, SQL Database Studio, MySQL Workbench, SQL Server Management Studio (SMSS) or Visual Studio for SQL, and a text editor IDE such as Notepad++ or Emacs.
With R-Brain, analytics projects across different languages can be easily organized and managed together in one common workspace. Although a few data science providers have similar features, R-Brain’s IDE provides quite a bit more functionality. Here is an IDE comparison provided by R-Brain. Additional data science platform and kernel comparison matrices are also available.
Getting started with R-Brain is quick and easy. It took me three minutes to set up my environment. I simply registered to create an account and then selected options for my workspace. If someone had shared an analytics workspace with me, it would have been listed as an available workspace in my Inventory. Customized workspace distribution is quite useful for teachers or enterprises that want to standardize analytics environments.
R-Brain currently offers four base workspace types: Intrinsic, Data Science, TensorFlow, and Spark. For my hands-on evaluation, I created a new Data Science workspace.
SQL, R and Python 3.
