The goal of big data is to find insights and make better decisions using data from traditional databases as well as from the fast-growing new sources of digital data, including the web, biological and industrial sensors, video, e-mail and social network communications.
But extracting timely business insights depends in large part on the availability and speed of the big data environment. Big data’s volume, variety and velocity can greatly increase the overall complexity of the application and infrastructure which in turn can impact performance. The reality is that big data applications and environments suffer from many of the performance challenges and bottlenecks that plague current distributed applications, though the volume of performance data can be much larger. This puts ROI from big data projects at risk.
Poorly performing big data applications and environments can impact the business and revenues when customers complain or business analytics are delayed or unavailable. For example, is the issue with a particular Hadoop MapReduce job? Data access or distribution? Bad code? Server or hardware? Network? Or the application itself? Trying to scale away the problem by adding more nodes, clusters or hardware can often be extensive and futile.
Ensuring the performance and availability of big data applications to meet business demands and satisfy end users requires a new generation of application performance management (APM). This approach must offer deep insight into systems in order to optimize compute and data distribution across nodes; assure job execution efficiency; identify I/O bottlenecks and tune CPU and memory consumption amongst thousands of nodes.
To this end, Compuware has announced pricing for the industry’s first deep transaction management solution for optimizing the performance of Apache Hadoop applications. Compuware APM’s dynaTrace Enterprise for Hadoop will be offered starting at just $1,000 per Hadoop Java Virtual Machine (JVM). This solution allows organizations to pinpoint the root causes of MapReduce job issues within minutes instead of days, optimizing Hadoop environments while driving significant cost savings.
- Zero-Configuration Instrumentation: Out-of-the-box dashboards for 100 percent deep visibility into Hadoop MapReduce performance, with no code changes required and easy to deploy and manage;
- One-Click Hotspot Analysis: Faster mean-time-to-resolve (MTTR) with one-click hotspot analysis of MapReduce jobs, including long-running and highly distributed jobs. See root cause in minutes instead of hours or days;
- Automated Performance Analytics: Optimize Hadoop environments and save costs with deep insight into how MapReduce jobs consume resources, scale across cluster and automated performance analytics from the task-level down to individual method execution times;
- Correlated Cluster Health Monitoring: Monitor Hadoop cluster overall and down to individual machines as well as monitor CPU, memory, disk, I/O and garbage collection to detect and correlate system health to job performance. Proactively fix issues before they impact SLAs; and
- Automatic MapReduce Error Correlation With Job, Task and Method level Detail: For faster MTTR than any other approach in the market.