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Unravel Data Partners with Databricks for Lakehouse Observability and FinOps

Unravel Data has joined the Databricks Partner Program to deliver AI-powered data observability into Databricks for granular visibility, performance optimizations, and cost governance of data pipelines and applications.

With this new partnership, Unravel and Databricks will collaborate on Go-To-Market (GTM) efforts to enable Databricks customers to leverage Unravel’s purpose-built AI for the Lakehouse for real-time, continuous insights and recommendations to speed time to value of data and AI products and ensure optimal ROI.

Unravel’s purpose-built AI for Databricks integrates with Lakehouse Monitoring and Lakehouse Observability to deliver performance and efficiency needed to achieve speed and scale for data analytics and AI products. Unravel’s integration with Unity Catalog enables Databricks users to speed up lakehouse transformation by providing real-time, AI-powered cost insights, code-level optimizations, accurate spending predictions, and performance recommendations to accelerate data pipelines and applications for greater returns on cloud data platform investments. Auto Actions and alerts help automate governance with proactive guardrails.

“Most organizations today are receiving unprecedented amounts of data from a staggering number of sources, and they’re struggling to manage it all, which can quickly lead to unpredictable cloud data spend. This combination of rapid lakehouse adoption and the hyperfocus companies have on leveraging AI/ML models for additional revenue and competitive advantage, brings the importance of data observability to the forefront,” said Kunal Agarwal, CEO and co-founder, Unravel Data. “Lakehouse customers who use Unravel can now achieve the agility required for AI/ML innovation while having the predictability and cost governance guardrails needed to ensure a strong ROI.”

Unravel’s purpose-built AI for Databricks delivers insights based on Unravel’s deep observability at the job, user, and code level to supply AI-driven cost efficiency recommendations, including compute provisioning, query performance, autoscaling efficiencies, and more.

Unravel for Databricks enables organizations to:

- Speed cloud transformation initiatives by having real-time cost visibility, predictive spend forecasting, and performance insights for their workloads

- Enhance time to market of new AI initiatives by mitigating potential pipeline bottlenecks and associated costs before they occur

- Better manage and optimize the ROI of data projects with customized dashboards and alerts that offer insights on spend, performance, and unit economics

Unravel’s integration with popular DevOps tools like GitHub and Azure DevOps provides actionability in CI/CD workflows by enabling early issue detection during the code-merge phase and providing developers real-time insights into potential financial impacts of their code changes. This results in fewer production issues and improved cost efficiency.

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Unravel Data Partners with Databricks for Lakehouse Observability and FinOps

Unravel Data has joined the Databricks Partner Program to deliver AI-powered data observability into Databricks for granular visibility, performance optimizations, and cost governance of data pipelines and applications.

With this new partnership, Unravel and Databricks will collaborate on Go-To-Market (GTM) efforts to enable Databricks customers to leverage Unravel’s purpose-built AI for the Lakehouse for real-time, continuous insights and recommendations to speed time to value of data and AI products and ensure optimal ROI.

Unravel’s purpose-built AI for Databricks integrates with Lakehouse Monitoring and Lakehouse Observability to deliver performance and efficiency needed to achieve speed and scale for data analytics and AI products. Unravel’s integration with Unity Catalog enables Databricks users to speed up lakehouse transformation by providing real-time, AI-powered cost insights, code-level optimizations, accurate spending predictions, and performance recommendations to accelerate data pipelines and applications for greater returns on cloud data platform investments. Auto Actions and alerts help automate governance with proactive guardrails.

“Most organizations today are receiving unprecedented amounts of data from a staggering number of sources, and they’re struggling to manage it all, which can quickly lead to unpredictable cloud data spend. This combination of rapid lakehouse adoption and the hyperfocus companies have on leveraging AI/ML models for additional revenue and competitive advantage, brings the importance of data observability to the forefront,” said Kunal Agarwal, CEO and co-founder, Unravel Data. “Lakehouse customers who use Unravel can now achieve the agility required for AI/ML innovation while having the predictability and cost governance guardrails needed to ensure a strong ROI.”

Unravel’s purpose-built AI for Databricks delivers insights based on Unravel’s deep observability at the job, user, and code level to supply AI-driven cost efficiency recommendations, including compute provisioning, query performance, autoscaling efficiencies, and more.

Unravel for Databricks enables organizations to:

- Speed cloud transformation initiatives by having real-time cost visibility, predictive spend forecasting, and performance insights for their workloads

- Enhance time to market of new AI initiatives by mitigating potential pipeline bottlenecks and associated costs before they occur

- Better manage and optimize the ROI of data projects with customized dashboards and alerts that offer insights on spend, performance, and unit economics

Unravel’s integration with popular DevOps tools like GitHub and Azure DevOps provides actionability in CI/CD workflows by enabling early issue detection during the code-merge phase and providing developers real-time insights into potential financial impacts of their code changes. This results in fewer production issues and improved cost efficiency.

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According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...