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Unravel for Azure Databricks Introduced

Unravel Data announced Unravel for Azure Databricks, a solution to deliver comprehensive monitoring, troubleshooting, and application performance management for Azure Databricks environments.

The new offering leverages AI to enable Azure Databricks customers to significantly improve performance of Spark jobs while providing unprecedented visibility into runtime behavior, resource usage, and cloud costs.

“Spark, Azure, and Azure Databricks have become foundational technologies in the big data landscape, with more and more Fortune 1000 organizations using them to build their modern data pipelines,” said Kunal Agarwal, CEO, Unravel Data. “Unravel is uniquely positioned to empower Azure Databricks customers to maximize the performance, reliability and return on investment of their Spark workloads.”

Unravel for Azure Databricks helps operationalize Spark apps on the platform: Azure 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.

“Unravel’s full-stack DataOps platform has already helped Azure customers get the most out of their cloud-based big data deployments. We’re excited to extend that relationship to Azure Databricks,” said Yatharth Gupta, principal group manager, Azure Data at Microsoft. “Unravel adds tremendous value by delivering an AI-powered solution for Azure Databricks customers that are looking to troubleshoot challenging operational issues and optimize cost and performance of their Azure Databricks workloads.”

Key features of Unravel for Azure Databricks include:

- Application Performance Management for Azure 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 Azure Databricks clusters

- Comprehensive reporting, alerting, and dashboards – Azure 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.

Azure Databricks is a Spark-based analytics platform optimized for Microsoft Azure. Azure Databricks provides one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.

An early access release of Unravel for Azure Databricks available now.

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Unravel for Azure Databricks Introduced

Unravel Data announced Unravel for Azure Databricks, a solution to deliver comprehensive monitoring, troubleshooting, and application performance management for Azure Databricks environments.

The new offering leverages AI to enable Azure Databricks customers to significantly improve performance of Spark jobs while providing unprecedented visibility into runtime behavior, resource usage, and cloud costs.

“Spark, Azure, and Azure Databricks have become foundational technologies in the big data landscape, with more and more Fortune 1000 organizations using them to build their modern data pipelines,” said Kunal Agarwal, CEO, Unravel Data. “Unravel is uniquely positioned to empower Azure Databricks customers to maximize the performance, reliability and return on investment of their Spark workloads.”

Unravel for Azure Databricks helps operationalize Spark apps on the platform: Azure 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.

“Unravel’s full-stack DataOps platform has already helped Azure customers get the most out of their cloud-based big data deployments. We’re excited to extend that relationship to Azure Databricks,” said Yatharth Gupta, principal group manager, Azure Data at Microsoft. “Unravel adds tremendous value by delivering an AI-powered solution for Azure Databricks customers that are looking to troubleshoot challenging operational issues and optimize cost and performance of their Azure Databricks workloads.”

Key features of Unravel for Azure Databricks include:

- Application Performance Management for Azure 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 Azure Databricks clusters

- Comprehensive reporting, alerting, and dashboards – Azure 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.

Azure Databricks is a Spark-based analytics platform optimized for Microsoft Azure. Azure Databricks provides one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.

An early access release of Unravel for Azure Databricks available now.

The Latest

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

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In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

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