A platform for collaborative data science at scale
For data scientists
Experiment faster. Use R, Python, or Scala with on-demand compute and secure access to Apache Spark™ and Apache Impala™
Work together. Share reproducible research with your whole team
Deploy with confidence. Get to production repeatably and without recoding
For IT professionals
Bring data science to your data. Give your team more freedom while reducing the risk and cost of silos
Secure by default. Leverage common security and governance across workloads
Flexible deployment. Run on-premises or in the cloud
Self-service data science
With Python, R, and Scala directly in the web browser, Cloudera Data Science Workbench (CDSW) delivers a self-service experience data scientists will love. Download and experiment with the latest libraries and frameworks in customizable project environments that work just like your laptop. Access any data, anywhere—from cloud object storage to data warehouses, Cloudera Data Science Workbench provides connectivity not only to CDH and HDP but also to the systems your data science teams rely on for analysis.
Automated data and analytics pipelines
Cloudera Data Science Workbench lets data scientists manage their own analytics pipelines, including built-in scheduling, monitoring, and email alerting. Quickly develop and prototype new machine learning projects and easily deploy them to production.
Quickly deploy models with confidence
A single, unified workflow lets you build, train, and deploy your own machine learning models. Experiments track each training run, for easy reproducibility. Share models as REST APIs with a few clicks, without expensive rewrites or complex DevOps knowledge.
Secure for the enterprise
Cloudera Data Science Workbench is secure and compliant by default, with support for full Hadoop authentication, authorization, encryption, and governance. Finally, data scientists can easily access Hadoop data and run Spark queries in a safe environment.
Multi-tenant and ready for the cloud
Break down analytic silos and drive more value from your enterprise data hub, whether on-premises or in the cloud. Collaborative, shareable project environments ensure that diverse data science teams can work together toward standard, reproducible research.
