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Concurrent Delivers Application Performance Management for Enterprise-Scale Big Data, Announces Driven 2.0

concurrent_logoConcurrent, Inc., the leader in data application infrastructure, today announced the next version of Driven, the industry’s leading application performance management solution for monitoring and managing enterprise-scale Big Data applications. Driven 2.0 represents an industry milestone by enabling 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.

Enterprise needs have not changed, said Gary Nakamura, CEO, Concurrent, Inc. “They want a comprehensive solution to monitor and manage their data processes. They want technical, operational, organizational and business-level context on every process. They want to measure how these processes are performing, how they are consuming resources and whether they are delivering or not – and if they aren’t, where is the issue? Driven equips enterprises with this and more, and is playing a critical role in the success of big data initiatives in the enterprise.”

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.

A proven application performance management solution for enterprise scale Big Data, enterprises rely on Driven to deliver against today’s complex data strategies. Eight of the top 10 financial services organizations use Driven to manage their big data applications, and Driven monitors applications responsible for hundreds of millions in revenues. 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 is available at http://www.driven.io/choose-trial.

 

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