Big-data application performance monitoring firm Unravel Data Systems Inc. today announced that it has raised $50 million in new funding to accelerate what it believes is the next generation of DataOps observability.
Third Point Ventures led the Series D round, with Bridge Bank, Menlo Ventures, Point 72, GGV Capital and Harmony Capital also participating. Including the new funding, the company has raised $107 million to date, including rounds of $15 million in 2018 and $35 million in 2019.
Founded in 2013, Unravel Data offers an artificial-intelligence powered automated performance management platform for big data across the full stack. The platform is said to transform the way businesses understand and optimize the performance and cost of their modern data applications – and the complex data pipelines that power those applications.
The platform unifies a process that previously required separate monitoring and administration tools, including application logs, Apache Sparkcontext, Cloudera Inc.’s Manager and MapR Technologies Inc.’s Control Systems. With a unified view across the entire data stack, Unravel says, it delivers a DataOps observability platform with AI, machine learning and advanced analytics to provide data teams with actionable recommendations.
Unravel Data guarantees the reliability and performance of apps, maximizes cost savings across storage, compute and users, and improves productivity in a self-service DataOps environment.
The company notes that the investment comes as large enterprises face the challenge of operating data pipelines for data products, advanced modeling and business-critical reporting. This is a time when the complexity of data systems is heightened by the shift to multicloud strategies and burdened by overprovisioned environments. As a result, data teams struggle to deliver data outcomes in a time-efficient manner and effectively manage the limitless rise in cloud compute and storage costs.
The new funding will be used to help connect the dots from every system in the modern data stack, including within and across the most popular data ecosystems such as Databricks Inc., Snowflake Inc., Amazon Web Services Inc.’s EMR, and Google LLC’s BigQuery and Dataproc.
“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,” Kunal Agarwal, chief executive officer of Unravel Data, said in a statement. “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.”