Unravel Data has said that it has been recognized as a Representative Vendor in the DataOps Market in the 2022 Gartner Market Guide for DataOps Tools.
“In recent years, organizations have witnessed significant increases in the number of data sources and data consumers as they deploy more applications and use cases. DataOps has risen as a set of best practices and technologies to streamline and efficiently handle this onslaught of new upcoming workloads,” said Sanjeev Mohan, principal with SanjMo.
“Just as DevOps helped streamline web application development and make software teams more productive, DataOps aims to do the same thing for data applications,” added Sanjeev.
According to a Gartner strategic planning assumption from this Market Guide, “by 2025, a data engineering team guided by DataOps practices and tools will be 10 times more productive than teams that do not use DataOps.” In this Market Guide for DataOps Tools, Gartner examines the DataOps market and explains the various capabilities of DataOps tools, paints a picture of the DataOps tool landscape, and offers recommendations.
“Data teams are struggling to keep up with the increased volume, velocity, variety, and complexity of their data applications and data pipelines. These teams are facing many of the same generic challenges that software teams did 10-plus years ago,” said Kunal Agarwal, CEO of Unravel Data. “We’re proud to be recognized by Gartner in the DataOps Market. Unravel Data provides unprecedented visibility across users’ data stacks, proactively troubleshooting and optimizing data workloads, and defining guardrails to govern costs and improve predictability.”
Today’s modern data stack is a complex collection of different systems, platforms, technologies, and environments. Enterprises need a DataOps solution that works with every type of workload and addresses the performance, cost, and quality issues facing data teams today. Founded by Kunal Agarwal and Shivnath Babu, Unravel Data was created on the notion that the exponential growth of data combined with the broad adoption of the public cloud requires an entirely new way to manage and optimize the data pipelines that support the real-time analytics needs of data-driven enterprises.
