Integrated visual environment for data science applications with Dataiku DSS 3
Dataiku introduces the integrated visual environment for the design and production of data science applications, batch or real-time with Dataiku DSS 3. This new major release of Dataiku DSS includes advanced data monitoring and an API-driven dedicated production node, which allows users to create concurrent data analytics packages for deployment and testing.
The new integrated visual environment in DSS 3 includes a dedicated production node feature that solves the problem of development environments typically being disconnected and incompatible with production environments. Users can now deploy, test, and roll-back (if needed) multiple instances of their data applications in all cycles of the data engineering process, from development to deployment. This allows the data team to independently design, build, run, and continuously improve their data products even while in they are running in production.
“To succeed in today's rapidly evolving data ecosystem, companies must continuously re-invent and deliver innovative data products. Unfortunately, in most organisations, there is a disconnect between development and production environments causing projects to either fail or to drag on for months beyond promised deadlines. But with the overwhelming plethora of new technologies and blooming skill sets, it doesn't have to be that way. That’s why Dataiku DSS 3 is so important. We’ve really designed the platform so that companies can deliver - and continue to improve - their data products efficiently,” explains Florian Douetteau, Dataiku’s CEO and co-founder.
Dataiku DSS 3 provides key features that are beneficial to "DataOps". Similar to DevOps, DataOps is a new profile arising in data-driven organisations whose goal is to improve the coordination between data application development and operation. The new DataOps features in Dataiku DSS 3 are:
Real-Time Model Deployment: Predictive models can be deployed and versioned and made available through a high-availability API.
Advanced Data Metrics Monitoring : Advanced monitoring of data allows the user to define ‘data boundaries,’ which the system can monitor and automatically generate an alert if there are any important divergences.
Enterprise Integration Scenarios:Dataiku augments its existing smart data flow reconstruction system with enterprise integration scenarios. The new integration scenario enables users to secure the production life-cycle of a predictive model, with features such as data quality check, model divergence check, trigger on an event, etc…
Resource Usage Monitoring: Data Ops can now track computing resource usage and data volume of their Data Science applications, across file systems, Hadoop, Cassandra, and Data warehouses.
In addition, Dataiku DSS 3 introduces new features to simplify the collaboration and documentation of data projects:
Enriched Version Control: Users will now be able to see the history of changes in their workflow which enables better project management and tracking of a project’s evolution.
Team Activity Dashboards: The data team manager has a visual representation of individual and team activity (commits, new recipes, new models, etc.) to give deep insights into overall project productivity and evolution.
Metadata management and Data Discovery: The data catalogue enables users and project managers of very large teams to manage and navigate many projects at once.
User Defined Data Types: Business entities and internal lingo can now be directly defined into Dataiku DSS. This allows for the automatic documentation of data projects and enables data validity to be checked against user defined business rules.
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More Information on Predictive Analysis Process
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.