PALO ALTO, Calif.--(BUSINESS WIRE)--Unravel Data, the only data operations platform providing full-stack visibility and AI-powered recommendations to drive more reliable performance in modern data applications, today announced Unravel for AWS Databricks, a solution to deliver comprehensive monitoring, troubleshooting, and application performance management for AWS Databricks environments. Unravel for AWS Databricks leverages Unravel’s AI-powered data operations platform to accelerate performance of Spark on AWS while providing unprecedented visibility into runtime behavior, resource usage, and cloud costs.
“As business needs evolve, data workloads are moving to a growing variety of settings, stretching across on-prem environments, public clouds, multiple clouds and a hybrid mix of all of these. It’s important that organizations can get the same performance, reliability and value out of their data applications no matter where they are,” said Kunal Agarwal, CEO, Unravel Data. “Unravel for AWS is our latest effort to expand the platform to accommodate Big Data wherever it exists. With this addition, Unravel now supports Databricks in both AWS and Azure, and the Unravel platform is broadly available in every major public cloud as well as on-premises and in hybrid settings. We were always committed to being infrastructure-agnostic and this is another milestone in that mission.”
The announcement is the latest development in a long relationship between Unravel and AWS. Unravel already supports Amazon EMR, as well as Cloudera/Hortonworks on IaaS for AWS. This release provides further support for customers deploying modern data apps on AWS. In addition, Unravel is an existing member of the AWS Partner Network and member of AWS global startup program.
AWS Databricks is a unified data analytics platform for accelerating innovation across data science, data engineering, and business analytics, integrated with AWS infrastructure. Unravel for AWS Databricks helps operationalize Spark apps on the platform: AWS Databricks customers will shorten the cycle of getting Spark applications into production by relying on the visibility, operational intelligence, and data driven insights and recommendations that only Unravel can provide. Users will enjoy greater productivity by eliminating the time spent on tedious, low value tasks such as log data collection, root cause analysis and application tuning.
Key features of Unravel for AWS Databricks include:
- Application Performance Management for AWS Databricks – Unravel delivers visibility and understanding of Spark applications, clusters, workflows, and the underlying software stack
- Automated root cause analysis of Spark apps – Unravel can automatically identify, diagnose, and remediate Spark jobs and the full Spark stack, achieving simpler and faster resolution of issues for Spark applications on AWS Databricks clusters
- Comprehensive reporting, alerting, and dashboards – AWS Databricks users can now enjoy detailed insights, plain-language recommendations, and a host of new dashboards, alerts, and reporting on chargeback accounting, cluster resource usage, Spark runtime behavior and much more
About Unravel Data
Unravel radically simplifies the way businesses understand and optimize the performance of their modern data applications – and the complex pipelines that power those applications. Providing a unified view across the entire stack, Unravel’s data operations platform leverages AI, machine learning, and advanced analytics to offer actionable recommendations and automation for tuning, troubleshooting, and improving performance – both today and tomorrow. By operationalizing how you do data, Unravel’s solutions support modern big data leaders, including Kaiser Permanente, Adobe, Deutsche Bank, Wayfair, and Neustar. The company is headquartered in Palo Alto, California, and is backed by Menlo Ventures, GGV Capital, M12, Point72 Ventures, Harmony Partners, Data Elite Ventures, and Jyoti Bansal. To learn more, visit unraveldata.com.
The name Unravel Data is a trademark of Unravel Data™. Other trade names used in this document are the properties of their respective owners.