Domino Data Science Platform and Cloudera Data Science Workbench are both in the data science platform category. Domino appears to have the advantage in collaboration and scalability, while Cloudera is stronger in integration within its data ecosystem.
Features: Domino focuses on collaborative data science workflows with robust project management, computational environment customization, and enhanced scalability. Cloudera integrates seamlessly with existing Cloudera services and provides strong support for machine learning models and automated pipeline management.
Ease of Deployment and Customer Service: Domino's cloud-optimized deployment model offers flexibility in resource management and comprehensive onboarding support. Cloudera performs well in private cloud or on-premises setups, benefiting from its integration with Cloudera's data platform, complemented by extensive documentation and support resources.
Pricing and ROI: Domino, while having a higher initial setup cost, is noted for delivering a strong ROI through enhanced productivity and collaboration. Cloudera offers competitive pricing, especially for existing customers who harness integrated solutions and support, leading to quicker value realization from their investment.
Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. With CDSW, organizations can research and experiment faster, deploy models easily and with confidence, as well as rely on the wider Cloudera platform to reduce the risks and costs of data science projects. Access any data anywhere – from cloud object storage to data warehouses, CDSW provides connectivity not only to CDH but the systems your data science teams rely on for analysis.
Domino provides a central system of record that keeps track of all data science activity across an organization. Domino helps data scientists seamlessly orchestrate AWS hardware and software toolkits, increase flexibility and innovation, and maintain required IT controls and standards. Organizations can automatically keep track of all data, tools, experiments, results, discussion, and models, as well as dramatically scale data science investments and impact decision-making across divisions. The platform helps organizations work faster, deploy results sooner, scale rapidly, and reduce regulatory and operational risk.
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