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Unravel for Azure Databricks Includes New Features

Unravel Data introduced new migration, cost analytics and architectural mapping capabilities for Unravel for Azure Databricks, which is now generally available from Unravel and in the Azure Marketplace.

The move further solidifies Unravel’s mission to support modern data workloads wherever they exist, whether on-premises, in the public cloud or a hybrid setting.

“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 migration intelligence to help customers assess their migration planning to Azure Databricks in version 4.5.5.0. Slice and dice migration support provides impact analysis by applications and workloads. It also features recommended cloud cluster topology and cost estimates by service-level agreement (SLA), as well as auto-scaling impact trend analysis as a result of cloud migration.

- Cost analytics – Unravel will soon add new cost management capabilities to help optimize Azure Databricks workloads as they scale. These new features include cost assurance, cost planning and cost forecasting tools. Together, these tools provide granular detail of individual jobs in Azure Databricks, providing visibility at the workspace, job, and job-run level to track costs or DBUs over time.

- Detailed architectural recommendations: 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.

In addition to being generally available directly from Unravel, Unravel for Azure Databricks is also available on the Azure Marketplace.

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Unravel for Azure Databricks Includes New Features

Unravel Data introduced new migration, cost analytics and architectural mapping capabilities for Unravel for Azure Databricks, which is now generally available from Unravel and in the Azure Marketplace.

The move further solidifies Unravel’s mission to support modern data workloads wherever they exist, whether on-premises, in the public cloud or a hybrid setting.

“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 migration intelligence to help customers assess their migration planning to Azure Databricks in version 4.5.5.0. Slice and dice migration support provides impact analysis by applications and workloads. It also features recommended cloud cluster topology and cost estimates by service-level agreement (SLA), as well as auto-scaling impact trend analysis as a result of cloud migration.

- Cost analytics – Unravel will soon add new cost management capabilities to help optimize Azure Databricks workloads as they scale. These new features include cost assurance, cost planning and cost forecasting tools. Together, these tools provide granular detail of individual jobs in Azure Databricks, providing visibility at the workspace, job, and job-run level to track costs or DBUs over time.

- Detailed architectural recommendations: 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.

In addition to being generally available directly from Unravel, Unravel for Azure Databricks is also available on the Azure Marketplace.

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.