What happened to mass to the cloud during the pandemic, IT systems are becoming increasingly complex. The modern data stack includes hundreds of tools for application development, data capture and integration, orchestration, analysis, and storage. And it’s getting bigger and more complicated every day. According to software-as-a-service application management startup Prodive, the average company had 254 internal devices as of last September, with most departments scrambling for 40 to 60 each.
To address these growing challenges, Kunal Agarwal and Shivnath Babu co-founded Unravel Data, a platform designed to help development teams gain visibility into the data stack, define security paths to solve and optimize data workloads, and control costs. As the token business continues to grow, Unravel today closed a $50 million Series D funding round led by Third Point Ventures with participation from Bridge Bank, Melo Ventures, Point72, GGV Capital and Harmony Capital, bringing the total raised to $107 million.
“Regardless of the industry an enterprise competes in, one thing they all have in common is the understanding that the ability to turn raw data into actionable insights is directly proportional to the ability to bring innovations to market,” Agarwal said. TechCrunch in an email interview. “As a result, despite the economic uncertainty caused by the pandemic, we have seen strong and sustained demand for both. [observability] Methodology in general and the Unravel platform in particular.
Agarwal and Babu met at Duke University, where Shivnath was a professor studying how to make data-intensive computing systems easier to manage. Agarwal was previously at Sun Microsystems, where he was a grid computing specialist and member of the sales team. The two say they saw an opportunity to create a platform that would take all the granularity of different big data workloads within an organization and deliver them in a single pane of glass.
Solve experiments to match details from stacks of data, then apply AI and machine learning to provide recommendations and insights on how to — in Agarwal’s words — “make things better.” For example, the platform automatically implements safeguards by sending alerts when something goes wrong, such as overruns and errors.
Solve a data web-based data tracking dashboard in action.
From what we capture and relate at the highest level—configuration, resources, containers, code, datasets, lineage, and dependencies—down to individual user or job or work class components in parallel, Unravel’s AI engines form a flexible foundation. Detect anomalies across multiple dimensions, with contextual awareness and provide actionable intelligence through suggestions and insights,” Agarwal said. “For example, if a task that normally takes three minutes to run now takes ten minutes, are we experiencing out-of-memory problems because the amount of data being processed has doubled? If so, why is there so much more data now? Where does the dataset come from? Who doubled the size? Is that intentional? What and how does it affect other dependent tasks? “
Unravel is essentially a data analytics platform, the technology investors have an insatiable appetite for. In one week last June, three data visualization startups — Cribble, Monte Carlo, and CoralLogics — raised more than $400 million in venture capital. Other big players in the space include performance management tools developer Observator, stream processing platform Edge Delta, data pipeline platform Manta and open observator platform Grafana Labs.
Agarwal doesn’t see much overlap between Unravel and application monitoring solutions like Datadog, Dynatrax and New Relic, which he recognizes solve a very different data orchestration problem. As for observational providers such as the aforementioned Monte Carlo, he asserts that data stacks only solve pieces of the puzzle and lack the modeling capabilities of Unravel’s product.
“New cloud technologies offer greater efficiency and innovation, but they come at the cost of increased complexity. It’s getting harder and harder for leaders to ensure they’re getting value and getting a return on their investment,” Agarwal said. As the stack becomes more complex, it becomes harder to untangle the wires to figure out what went wrong and how to fix it. Unravel makes self-service troubleshooting and optimization easier for different data team members with different skill sets and skill levels.
Agarwal declined to disclose Unravel’s revenue or the size of the company’s customer base. But Adobe and Deutsche Bank are among its clients, as well as grocery chain Kroger’s 84.51° data analytics division, he said.
With an eye on the horizon, Agarwal said proceeds from the Series D will be used to streamline Unravel’s operations, build APIs for an expanded number of apps, and “double” the size of Unravel’s engineering team. He wouldn’t commit to immediate hiring plans, but indicated that Unravel, which currently has more than 100 employees across the US, Europe and India, is hiring for technical and operations roles.
