Concurrent announced the latest release of Driven, an application performance management solution for monitoring and managing enterprise-scale Big Data applications.
Driven 2.0 enabes application performance monitoring and management across heterogeneous Hadoop and Spark environments within a single, comprehensive solution.
As industries identify and continue to refine Big Data use cases, new development frameworks, tools, technologies and compute engines proliferate to allow project teams to implement the best solution for their specific use case. However, with more choice comes greater complexity. This is creating new requirements for DevOps, data operations, development teams and data professionals, who need visibility, measurement and control of data processing performance at scale.
Driven 2.0 ensures the highest fidelity and richest detail for application performance monitoring, troubleshooting, dependency tracking and service-level adherence of Apache Hive, MapReduce, Cascading, Scalding and, with today’s new release, Spark applications. No other single solution on the market delivers this level of coverage to empower teams with continuous visibility and traceability from development through production.
Key features of Driven 2.0 include:
- Support for Apache Spark: Enterprises can now seamlessly and transparently collect all the operational intelligence for Apache Spark applications in Driven. Currently in beta, new Spark support provides the comprehensive performance management required to deliver and maintain production Spark data processes.
- Redesigned application analytics and custom views: Driven delivers new capabilities to segment operational metadata and create customized views and dashboards for more concise information delivery to the enterprise user. Additionally, Driven features new and more comprehensive visualization and navigation of applications for a highly intuitive view of all applications and transaction history. Users now have the ability to drill down in real time or to specific time periods in history and view the health of an application or clusters or the processes associated with a specific organization.
- Deeper search capabilities: Because data processes can be unwieldy and complex, comprised of hundreds of steps, pinpointing where something was executed or went wrong in an application can be time consuming and expensive. Whether fulfilling an audit request, debugging an application, looking for a slow down or searching for dependencies, the new search capabilities in Driven enable enterprise users to quickly find specific units of work and progress to satisfy their needs.
Driven delivers benefits to the enterprise including accelerated application development cycles, immediate application failure diagnosis, improved application performance, easier audit reporting and reduced cluster utilization costs.
Driven 2.0 will be generally available in November.
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.