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Unravel Data Raises $50 Million Series D Funding

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.

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Unravel Data Raises $50 Million Series D Funding

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 live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.