This week, Zoomdata announced a new version of its big data analytics and visualization platform. Version 2.6 has several new features, including an API for landing streaming data, smart push-down processing, execution on external Spark clusters, and dashboard features.

In company news, Zoomdata recently closed its first two seven-figure customers, opened a legal entity in Japan called Zoomdata Japan K.K., and attained multiple partners, including StrategyCore, Atos, Infosys, SAP, Informatica, and Deloitte.

Zoomdata also announced that it will now release updates every four weeks, rather than two or three times a year. Zoomdata executives want to increase the velocity at which new features are added in anticipation of a SaaS version of Zoomdata.

Next Generation Streaming Analytics

Zoomdata has always had streaming analytics. And its data DVR capability enables customers to rewind and pause real-time visualizations. However, before now, customers took data out of a streaming engine, filtered it, and put it in a high performance store with historical data. That can be a lot of work, especially for a small company.

Now, Zoomdata has a new upload API that enables organizations to blend and land streaming data from its source into a variety of systems, including Kudu, MemSQL, and Google BigQuery, that contain historical data, all from the Zoomdata platform. This reduces the amount of time it takes to land streaming data alongside historical data, so customers can set up streaming analytics faster.

Smart Push-down Processing For Derived Fields

Traditionally, Zoomdata customers do calculations and data fusion within Spark to produce aggregate and filtered data. This decreases row-level data detail. However, sometimes customers need that detail for certain calculations. Now, with smart push-down processing, Zoomdata knows if processing, whether it’s binning, case logic, or formulas, requires row-level detail and pushes it down to the source.

In-cluster Execution in Spark

Zoomdata servers often run on one or a few Spark nodes. But some customers already have large Spark clusters in place. This feature enables these customers to configure Zoomdata to leverage their existing Spark cluster, making it easier for them to scale out.

Additional Dashboard Features

Customers can now use Zoomdata to create metric targets and visualize them. In addition, dashboards are responsive, so displays adjust automatically to the size of the screen. For example, as the screen size shrinks the dashboard display will drop legends and titles to make it more legible. This is good for dashboards with many visualization, presentations on large screens, and mobile users.

With these features, Zoomdata makes it easier to set up streaming analytics, take advantage of existing Spark clusters, attain row-level detail for processing, and present dashboards on small and large displays. To find out more about Zoomdata, click here.