Cloud and on-premises enterprise solutions


(A cloud data science platform for development, sharing and publications)

- Advanced IDE for R, Python and SQL with language support
- Support for Shiny, Bokeh, RMarkdown and notebooks
- Debugger, data and environment view
- Custom workspace packages
- Multi-user, multi-tenant containerized platform
- Private cloud installation and management
- Advanced sharing and collaboration
- Private community for models and applications
- Powerful admin control
- Service Level Agreement (SLA)
- 24/7 support
- Training programs
- Custom designed platform, security protocols and APIs

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RIDE Server

(Data science IDE server for cloud and on-premises)

- Advanced IDE for R, Python and SQL with language support
- Access IDE from anywhere via a web browser
- Support for Shiny, Bokeh, RMarkdown and notebooks
- Debugger, data and environment view
- multi-user, multi-tenant, scalable and containerized workspaces
- Works on any server that Docker works including Linux and Windows
- Run multiple analyses/worskpaces in parallel
- Powerful admin panel for defining resources
- Docker hub integration for using customized stacks
- LDAP, Active Directory, and OAuth Accounts
- Encrypt traffic using SSL and restrict client IP addresses
- Review historical usage and server logs
- Custom designed workspace stack (with additional charges)
- Integration in other platforms (with additional charges)
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Frequently Asked Questions
What is the main difference between RIDE and RStudio?

RIDE is a modern IDE with full language support for R, Python and SQL. You can develop in notebooks or text editor with content assitant, signature help, data and environment view, and debugger. It means that you do not change or pay extra license for your IDE for developing in these languages. Also, RIDE Server is a containerized platform that provides a secure and stable environment for all developers. It means that each project is a workspace with its own setup and configuration. 

What is the difference between RIDE and Jupyter notebooks or Jupyter lab?

First of all, RIDE has its own R Kernel. As a result, users are able to develop and run Shiny Apps, RMarkdown and visual debugger conveniently. They can also enjoy the extra services of data and environemnt view. Developers can use all these services in notebooks as well. Also, RIDE has a state of the art text editor with advanced features such as hovering, context menu and content assistant that makes development an enjoyable experience. These services are also availble for Python and SQL which are not available in Jupyter lab. 

Can I develop and run Shiny, RMarkdown or Bokeh apps in RIDE?

Yes of course. We support all these frameworks and update our service for those come to the market.

Do you have special discount for universities and small businesses?

Yes, absolutely. Please contact us to learn about special discounts for educational instituitions and small businesses.

Can we deploy RIDE Server on the cloud?

Yes of course. You can run RIDE Server anywhere that docker runs including cloud instances. For AWS, 1-Click deployment with hourly / annual licensing is coming soon. For other cloud services, you can follow the instruction of deployment on servers.

Can we deploy RIDE DSP on any cloud service?

No. RIDE DSP is avaiable on AWS only. 1-click installation for RIDE DSP is coming soon, in the meantime we setup RIDE DSP on your VPC by our support team.

Does RIDE DSP and RIDE Server support Python 2?

Yes. RIDE Server and RIDE DSP support Python 2 and 3, R and SQL. However, in contrast to Python 3, Python 2 service does not come with debugger and data view.

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