Unravel Data, a data operations platform providing full-stack visibility and AI-powered recommendations, is releasing new migration, cost analytics, and architectural mapping capabilities for Unravel for Azure Databricks.
“With more and more big data deployments moving to the public cloud, Unravel has spent the last several years helping to simplify the process of cloud migration as well as improving the management and optimization of modern data workloads once in the cloud. We have recently introduced platforms for all major public cloud platforms,” said Bala Venkatrao, chief product officer, Unravel Data. “This release, highlighted by the industry’s only slice and dice migration capabilities, makes it easier than ever to move data workloads to Azure Databricks, while minimizing costs and increasing performance. The platform also allows enterprises to unify their data pipelines end-to-end, such as Azure Databricks and Azure HDInsight.”
Unravel for Azure Databricks delivers comprehensive monitoring, troubleshooting, and application performance management for Azure Databricks environments.
The new additions to the platform include:
- Slice and dice migration support – Unravel now includes robust migration intelligence to help customers assess their migration planning to Azure Databricks in version 18.104.22.168.
- Cost analytics – Unravel will soon add new cost management capabilities to help optimize Azure Databricks workloads as they scale.
- Detailed architectural recommendation–Unravel for Azure Databricks will soon include right-sizing, a feature that recommends virtual machine or workload types that will achieve the same performance on cheaper clusters.
Unravel for Azure Databricks helps operationalize Spark apps on the platform: Azure Databricks customers can 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 enjoy greater productivity by eliminating the time spent on tedious, low value tasks such as log data collection, root cause analysis and application tuning.
For more information about this news, visit https://unraveldata.com/.