Startup Zoomdata Raises $17M to Push Real-Time "Sharpening" Approach to Big Data

Big data analytics startup Zoomdata announced a $17 million Series B funding round led by Accel Partners. The two-year-old Reston, Virginia-based startup specializes in providing what it describes as extremely fast big data exploration and visualizations.

The company has patented a big data technology it describes as real-time data "sharpening," which provides an instant approximated sketch of query results and then streams continuous updates to the visualization.

Existing investors from Zoomdata's 2013 $4.1 million Series A in 2013, including NEA, Columbus Nova Technology Partners, Razor’s Edge Ventures and B7m joined the latest round. The company said it will use the funds to expand its sales and marketing teams and accelerate product development.

Its big data technology comes as part of a platform that includes a suite of pre-built connectors to popular data sources, such as Hadoop, Cloudera Impala, Redshift, MongoDB, Apache Spark and many others. Zoomdata says it has 20 enterprise customers, including a major accounting firm that uses its product to give customers real-time access to interact with and explore the latest retail pricing trends from a MongoDB datastore.

On the presentation side Zoomdata visualizations are offered in an HTML5 touch-first interface, where the company says users can explore billions of records in seconds via a web browser on PCs, smartphones and tablets.

Zoomdata founder and CEO Justin Langseth said, "With the rapid adoption of modern Hadoop, NoSQL and Spark datastores, we saw an opportunity to disrupt the legacy market for enterprise and embedded reporting, dashboarding and analytics with a powerful visual platform designed for the business user. We are making it faster and easier to interact with big data through two key innovations: our patented micro-query technology, combined with our stream processing engine, allows Zoomdata to render big data into compelling visual views within seconds, tapping directly into both historical and real-time data across both legacy and modern data stores."