IT systems are becoming more complex, with the mass migration to the cloud during the pandemic. The modern data stack consists of hundreds of tools for app development, data capture and integration, orchestration, analysis, and storage. And it’s getting bigger and more complex by the day. according to For SaaS app management startup Productiv, the average company had 254 internal tools as of September last year, with most departments using between 40 and 60 tools each.
To tackle the growing challenge, Kunal Agarwal and Shivnath Babu co-founded. Unraveling the datais a platform designed to give developer teams visibility across the data stack, troubleshoot and optimize data workloads, and define guardrails to control costs. Unravel today closed a $50 million Series D funding round led by Third Point Ventures, with participation from Bridge Bank, Menlo Ventures, Point 72, GGV Capital and Harmony Capital, totaling $107 million. procured.
“Regardless of the industries in which companies compete, what they have in common is the understanding that their ability to transform raw data into actionable insights is directly proportional to their ability to bring new innovations to market,” Agarwal said. Told. TechCrunch emailed him an interview. “Therefore, despite the economic uncertainty brought on by the pandemic, we [observability] The methodology in general, and the Unravel platform in particular. ”
Agarwal and Babu met at Duke University. There, Shivnath was a tenured professor researching how to make data-intensive computing systems more manageable. Agarwal previously worked at Sun Microsystems, Grid He was a computing specialist and member of the sales team. The two say they saw an opportunity to create a platform that captures all the granularities of different big data workloads across the organization and presents them on a single screen.
Unravel tries to correlate details from the data stack, then applies 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 guardrails against cost overruns, errors, etc. and sends alerts when problems arise.
“It captures and correlates details such as configuration, resources, containers, code, datasets, lineage, dependencies, etc. Down to the parts, Unravel’s AI engine establishes a dynamic baseline, detecting anomalies with contextual awareness across multiple dimensions and delivering actionable intelligence through recommendations and insights,” Agarwal said. said. “For example, if a job that normally takes 3 minutes to run suddenly takes 10 minutes, is it because the size of the data being processed has doubled, causing an out-of-memory problem? If so, why are there so many now? Where did that dataset come from?Who doubled its size?Is it intentional?What does it do to other downstream dependent jobs? How will it affect you?”
Unravel is essentially a data observability platform, a technology that investors seem to have an insatiable appetite for. during the week Load Three data observability startups in June — crib, monte carlo When Coralogix — Raised over $400 million in venture capital.Other big players in this space include developers of performance management tools observationa stream processing platform edge deltadata lineage platform manta rays Open Observability Platform grafana lab.
Agarwal doesn’t see much overlap between Unravel and app monitoring solutions like Datadog, Dynatrace, and New Relic. We recognize that they address very different data orchestration problems. As for observability his vendor, such as the aforementioned Monte Carlo data stack, he just solves a piece of the puzzle, he argues, and lacks the modeling capabilities of Unravel’s product.
“New cloud technologies increase agility and innovation, but they also add complexity. ‘, said Agarwal. “Many organizations are finding their data migrations bogged down due to budget overruns and skyrocketing costs. Unravel makes self-service troubleshooting and optimization easier for different members of your data team with different skill sets and levels of expertise. increase.”
Agarwal declined to disclose Unravel’s revenue or the size of the company’s customer base. But he said Adobe and Deutsche Bank are its customers, including his 84.51° data analytics subsidiary of grocery chain Kroger.
Looking to the horizon, Agarwal said revenue from Series D will go to scaling Unravel’s operations, building APIs to ingest data from an expanded number of apps, and “doubling” the size of Unravel’s engineering team. said it would be filled. He hasn’t committed to immediate hiring plans, but said his Unravel, which currently has more than 100 employees in the US, Europe and India, is hiring for technical and operational positions. .