Unravel Data closed a $50 million Series D round of funding to accelerate the next generation of DataOps observability.
The round was led by Third Point Ventures, with participation from Bridge Bank and existing investors that include Menlo Ventures, Point72 Ventures, GGV Capital, and Harmony Capital, bringing the total amount of funding raised by Unravel Data to $107 million.
Unravel Data will use the investment to extend the Unravel Platform to help connect the dots from every system in the modern data stack within and across the most popular data ecosystems, including Databricks, Snowflake, Amazon EMR, BigQuery, and Dataproc. As the number of systems and data pipelines escalate, an entirely new way to manage and optimize the data pipelines that support the real-time analytics ambitions of the data-driven enterprise is needed.
“The DataOps observability market is poised to explode as enterprises invest in building data products that increase customers, revenue, and efficiencies,” said Curtis McKee, Partner at Third Point Ventures. “We’re excited to partner with Unravel Data, as the company has paved the way and established a proven track record of success helping some of the world’s most recognized brands simplify their data operations so they can bring new data-driven innovations to market.”
“Data engineers and data scientists currently spend more than half their day debugging and troubleshooting issues on the thousands of data pipelines in their environment,” said Kunal Agarwal, CEO of Unravel Data. “Just as the DevOps market united the practice of software development and operations a decade ago to transform the application lifecycle, data teams require the same kind of full-stack visibility, automation, and actionable intelligence that meet their needs around data pipeline performance, cost, and quality.”
Founded by Big Data pioneers Kunal Agarwal and Dr. Shivnath Babu, Unravel Data was born from the realization that the exponential growth of data combined with the broad adoption of the public cloud would require an entirely new way to manage and optimize the data pipelines that support the real-time analytics ambitions of the data-driven enterprise. Numerous Fortune 100 companies, including two of the top five global pharmaceutical companies and three of the top 10 financial institutions in the world, rely on Unravel Data to gain unprecedented visibility across their data stacks, proactively troubleshoot and optimize their data workloads, and define guardrails to govern costs and improve predictability. Customers who have deployed Unravel have been able to double productivity of data teams and ensure data applications run on time, while being able to scale cost efficiently on the cloud.
As part of the new funding, Curtis McKee, Partner at Third Point Ventures, will be joining Unravel Data’s Board of Directors.
The Latest
In Part 1 of this two-part series, I defined multi-CDN and explored how and why this approach is used by streaming services, e-commerce platforms, gaming companies and global enterprises for fast and reliable content delivery ... Now, in Part 2 of the series, I'll explore one of the biggest challenges of multi-CDN: observability.
CDNs consist of geographically distributed data centers with servers that cache and serve content close to end users to reduce latency and improve load times. Each data center is strategically placed so that digital signals can rapidly travel from one "point of presence" to the next, getting the digital signal to the viewer as fast as possible ... Multi-CDN refers to the strategy of utilizing multiple CDNs to deliver digital content across the internet ...
We surveyed IT professionals on their attitudes and practices regarding using Generative AI with databases. We asked how they are layering the technology in with their systems, where it's working the best for them, and what their concerns are ...
40% of generative AI (GenAI) solutions will be multimodal (text, image, audio and video) by 2027, up from 1% in 2023, according to Gartner ...
Today's digital business landscape evolves rapidly ... Among the areas primed for innovation, the long-standing ticket-based IT support model stands out as particularly outdated. Emerging as a game-changer, the concept of the "ticketless enterprise" promises to shift IT management from a reactive stance to a proactive approach ...
In MEAN TIME TO INSIGHT Episode 10, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Generative AI ...
By 2026, 30% of enterprises will automate more than half of their network activities, an increase from under 10% in mid-2023, according to Gartner ...
A recent report by Enterprise Management Associates (EMA) reveals that nearly 95% of organizations use a combination of do-it-yourself (DIY) and vendor solutions for network automation, yet only 28% believe they have successfully implemented their automation strategy. Why is this mixed approach so popular if many engineers feel that their overall program is not successful? ...
As AI improves and strengthens various product innovations and technology functions, it's also influencing and infiltrating the observability space ... Observability helps translate technical stability into customer satisfaction and business success and AI amplifies this by driving continuous improvement at scale ...
Technical debt is a pressing issue for many organizations, stifling innovation and leading to costly inefficiencies ... Despite these challenges, 90% of IT leaders are planning to boost their spending on emerging technologies like AI in 2025 ... As budget season approaches, it's important for IT leaders to address technical debt to ensure that their 2025 budgets are allocated effectively and support successful technology adoption ...
