Unravel Data introduced a performance management solution for the Google Cloud Dataproc platform that makes data workloads running on the top of the platform simpler to use and cheaper to run.
Unravel for Cloud Dataproc, which is available immediately, can improve the productivity of data teams with a simple and intelligent self-service performance management capability, helping DataOps teams:
- Optimize data pipeline performance and ensure application SLAs are adhered to
- Monitor and automatically fix slow, inefficient and failing Spark, Hive, HBase and Kafka workloads
- Maximize cost savings by containing resource-hogging users or applications
- Get a detailed chargeback view to understand which users or departments are utilizing the system resources
For enterprises powered by modern data applications that rely on distributed data systems, the Unravel platform accelerates new cloud workload adoption by operationalizing a reliable data infrastructure, and it ensures enforceable SLAs and lower compute and I/O costs, while drastically lowering storage costs. Furthermore, it reduces operational overhead through rapid mean time to identification (MTTI) and mean time to resolution (MTTR), enabled by unified observability and AIOps capabilities.
“Unravel simplifies the management of data apps wherever they reside - on-premises, in a public cloud, or in a hybrid mix of the two. Extending our platform to Google Cloud Dataproc marks another milestone on our roadmap to radically simplify data operations and accelerate cloud adoption,” said Kunal Agarwal, CEO, Unravel Data. “As enterprises plan and execute their migrations to the cloud, Unravel enables operations and app development teams to improve the performance and reduce the risks commonly associated with these migrations.”
In addition to DataOps optimization, Unravel provides a cloud migration assessment offering to help organizations move data workloads to Google Cloud faster and with lower cost. Unravel has built a goal-driven and adaptive solution that uniquely provides comprehensive details of the source environment and applications running on it, identifies workloads suitable for the cloud and determines the optimal cloud topology based on business strategy, and then computes the anticipated hourly costs. The assessment also provides actionable recommendations to improve application performance and enables cloud capacity planning and chargeback reporting, as well as other critical insights.
“We’re seeing an increased adoption of GCP services for cloud-native workloads as well as on-premises workloads that are targets for cloud migration. Unravel’s full-stack DataOps platform can simplify and speed up the migration of data-centric workloads to GCP giving customers peace of mind by minimizing downtime and lowering risk,” said Mike Leone, Senior Analyst, Enterprise Strategy Group. “Unravel adds operational and business value by delivering actionable recommendations for Dataproc customers. Additionally, the platform can troubleshoot and mitigate migration and operational issues to boost savings and performance for Cloud Dataproc workloads.”
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 ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
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 ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...
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 ...
Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...