Today we are excited to be announcing our January 2018 release. It is available as a download for on-premises usage as well as in the form of ready-to-run images for AWS, Azure and GCP. This release is the culmination of more than a years worth of planning and development. Existing customers who look under the covers will see this coded as version 2.6. With this release we have also switched to a monthly update release train, hence the new simple name - our January 2018 release.
In this release we have focused on some significant architectural enhancements as well as a big investment in analytical ease-of-use - strengthening Zoomdata’s position as the modern BI solution of choice. As older tools remain tied to older architectures and workflows, the need for Modern BI is clearer everyday. Moving data in order to analyze it is slow, and nearly always comes with a loss of fidelity. Additionally, more and more data is available as streams. Given that our goal is to deliver the fastest time-to-insight, cutting out unnecessary data movement and processing is key. This release delivers on that promise with several powerful new and enhanced capabilities for the analysis of streaming data, smart tiering of analytic processing, and empowering end-users to perform rapid analysis.
Breaking it down into big features, and ignoring the small ones - which are too numerous to list, here are the highlights:
- A new Smart Streaming interface, which easily enables connectivity to any streaming data source. Zoomdata can then blend the streamed data with historical data on-the-fly to drive real-time insights in the context of what has happened in prior minutes, hours, days and months. Zoomdata can connect to any fast data store where streaming data is being landed, or allow users to land streaming data directly through a simple streaming upload API
- Enhanced smart push-down processing, or tiering, of analytic calculations and derived fields. This enables new types of operations and business logic on detailed data at scale. Zoomdata now supports row-level as well as aggregate functions on data, and intelligently either pushes the calculation down to the data source, or leverages Spark to accelerate the calculation locally. The product documentation contains examples of many of the possible row-level calculations that are supported
- The ability to now use an existing external YARN managed Spark cluster of any size. Coupled with more capabilities extracted as microservices and centralized logging control, this enables in-cluster operations and data fusion at unprecedented levels of scale, while leveraging existing investments in enterprise infrastructure and skills
- Interactive dashboard layout management and visualizations that respond to the available real-estate. This enables dense self-service experiences and dashboard layouts that deliver insights in an optimal way. For users this means that it is even easier to construct dashboards that maximize information delivery in an efficient and responsive format. This is accompanied by many other usability related enhancements such as enhanced data formatting, new scorecarding KPIs and contextual linking between dashboards
- Numerous developer enhancements including new APIs and a brand new command line interface to create and manage custom charts using the development environment of their choice
- Even more enterprise-grade security, authentication and authorization enhancements
Details of all of these can be found in the release notes.