Unravel Data Secures $7M to Accelerate Big Data Applications

Unravel Data Secures $7M to Accelerate Big Data Applications

California-based data application and systems management solution provider Unravel Data recently announced a funding round worth $7 million. Unravel Data offers a full-stack performance intelligence platform for optimizing Big Data operations. Unravel Data accelerates all applications in a Big Data stack or cluster, optimizes multi-tenant resource utilization and provides operations intelligence, all from a single platform, delivering the full value of Big Data by resolving complex issues across the stack.

Unravel Data’s team of Big Data engineers and experts have been working behind the scenes to bring a single source of performance and operations intelligence for the entire modern data stack to the market for some time. With financial support from Menlo Ventures and Data Elite Ventures, along with guidance from an impressive group of top industry professionals, Unravel Data is on pace to deliver operations, developers, line of business managers, and those in emerging roles with the performance and operations visibility necessary to effectively and efficiently optimize Big Data applications from within a single platform.

Shivnath Babu, co-founder at Unrtavel Data, adds: “The rapid adoption of critical distributed technologies such as Hadoop, Spark, and Kafka into the Big Data stack has made the need for Unravel Data even greater. It’s difficult to determine whether an application is not performing at its peak because of bad code, data partitioning, system configuration settings, resource allocation or infrastructure issues. Unravel Data resolves these challenges immediately, thereby eliminating 90 percent of the time previously spent to identify and mitigate complex issues across the stack.”

Unravel Data is available for on-premise, cloud or hybrid Big Data deployments, and currently supports Hadoop, Spark, and Kafka. The company has plans in the works to expand support for other systems such as for data ingestion, NoSQL systems, and MPP systems.

Read the full press release.

Timothy King
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