Cloudera Data Science Workbench is secure and compliant, with support for Hadoop authentication, authorization, encryption, and governance.
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The platform operates directly in the web browser with Python, R, and Scala, giving users the ability to download and experiment with libraries and frameworks in customizable project environments.
“We are entering the golden age of and it’s all about the data. However, data scientists continue to struggle to build and test new analytics projects as fast as they would like, particularly in large scale environments,” Cloudera Products Senior Vice President Charles Zedlewski said in a statement. “The Data Science Workbench is a self-service tool that accelerates the ability to build, scale and deploy solutions.”
Making Collaborations Easier
“This means that data scientists now have the freedom to share, collaborate and manage their data in a way that best suits them and their enterprise, resulting in an easier and faster path to production.” Cloudera Data Science Workbench integrates with many existing frameworks including BigDL, a library for Apache Spark, open sourced by Intel. BigDL works directly within Cloudera’s Data Science Workbench and is built to run on distributed Spark/Hadoop infrastructure and performance-optimized to run on Intel Xeon processors.
Allowing for Integration into Existing Systems
The integration of BigDL into Data Science Workbench allows organizations to leverage libraries and tactics on CPU architecture without having to add additional hardware or separate environments. BigDL and Data Science Workbench gives organizations a way to create native Spark data science pipelines and integrate them with BigDL and other Spark/Hadoop components. “Enterprise customers require a cohesive platform to scale their analytics solutions and maximize their investments,” Intel VP and General Manager of System Technologies and Optimization in the Software and Services Group Michael Greene said in a statement. “BigDL’s native integration with Apache Spark brings the world of to the Apache Spark ecosystem and higher value to enterprise customers.”