The Social Coding Environment for Big Data Analytics & Visualization
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Exploration
Interactive, web-based environment to explore data on an interactive command line, including visualization and interactive graphics.
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Open Source
Code is available on Github –
collaborators and contributors welcome! -
Collaboration
Social coding integrated development environment (IDE): search, run, comment, fork, modify and share code and analyses - deployed as web services, web pages or dashboards.
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In the Cloud
Accessible from any connected device or computer anywhere, scalable power of distributed computing.
“Recently, the American Business Awards recognized RCloud as one of the Technical Innovations of the Year, and the International Business Awards recognized it as one of the Best New Products or Services of the Year in Software. Additionally, InfoWorld named RCloud as one of the best open source big data tools in their 2014 Bossie Awards.„
Features & Benefits
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Collaboration
RCloud is a social coding environment, designed to jumpstart your work. With RCloud you can rate and share code with other developers. You can form social circles around specific topic areas, and search for similar, relevant work, so you don’t have to recreate the wheel. Using RCloud to collaborate with other developers, you can dynamically manage content and link together various elements of an analysis.
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Deployment as Web service
RCloud is readily accessible through a standard web browser. It uses web-based notebooks to document and provide data analysis. This means you can access the RCloud environment, notebooks, results, and visualizations from anywhere.
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Work from anywhere
You can access RCloud from anywhere and any connected device. You might close an RCloud notebook at work and then open it again at home. Developers collaborating together can open the same notebook to see what each other is doing and insert a new step or modify an existing one.
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Scalability
RCloud gives you superfast interactions with data in HDFS or other systems. This is possible because we built in a chunk-wise compute + combine paradigm via customized functions for fast I/O.
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Distributed computing
RCloud uses both parallel and distributed processing to handle the high throughput computing and data management needs required for the immense data sets that are now commonly available in big data problems.
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Versioning/forking
We built RCloud with collaboration and transparency in mind. The documentation processes for RCloud’s web-based notebooks make it easy for developers to create a new version or fork existing code. You can see what packages and code, data sources and servers were used each step along the way – along with more detailed text and comments where needed. If you want to reuse an RCloud notebook, you can simply swap in different data sources, change the packages or code, or specify a different server.
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Multiple languages
RCloud currently supports R and Python programming languages.
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Integration of analysis language with UI
Both data scientists and business executives can use RCloud. We keep the interaction with the distributed computing system in the background, so the user does not need to write complex map/reduce code directly. Analyses can also be delivered via web services to allow for use and interaction with non-technical colleagues. This makes it easy to analyze and share results with anyone from business executives to project managers to fellow data scientists.