Dataiku DSS 3.1 Visual Machine Learning and support for Scala

Dataiku, released Dataiku DSS 3.1, which enables transformations in Apache Spark's Scala, adds additional external integrations, an improved UX interface, and includes 5 machine learning engines in its visual analysis section.Dataiku DSS 3.1 introduces new visual machine learning engines that allow users to create incredibly powerful predictive applications within a code-free interface. Users of all skill levels can now leverage HPE Vertica machine learning, H2O Sparkling Water, MLlib, Scikit-Learn, and XGBoost directly from within the visual analysis section of Dataiku DSS 3.1 to apply powerful machine learning algorithms to their data science projects without having to write a single line of code.

The blending of visual code-free and free-form code-based transformations is one of the main strengths of Dataiku DSS for the prototyping and production of data applications. In addition to Python, R, SQL, Hive, Impala, and Pig, Dataiku DSS 3.1 now enables Apache Spark users to write transformations and interactive notebooks in Scala, bringing the power of Spark's native and most performant language to the data teams using Dataiku DSS.

"With Dataiku DSS 3.1, we continue to bridge the gap between day to day analytic needs and the latest cutting edge data science technologies," said Florian Douetteau, CEO and co-founder of Dataiku. "By adding additional machine learning engines and enabling development in Scala, we are bringing even more tools to the table. This allows our users to build the best and most dynamic data science applications - quickly. All of the new features in this release add to our goal of being a complete, end-to-end platform for the creation, development, and deployment of predictive analytics solutions for any organization."

Additional features include: New external databases - Integration with IBM Netezza, Hana, and Big Query. Improved UX - Fluid navigation and project dependencies for an improved user experience. Seamless Integration with Tableau – Users can extend Dataiku DSS compatibility by creating custom export formats for datasets, including Tableau .tde files.

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More Information on Predictive Analysis Process

Predictive Analytics Process Flow

For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.