One Tool to Optimize, Troubleshoot & Analyze Performance
Unravel provides a single tool to do Application Performance Management for Big Data
Unravel doesn’t just report on usage data into graphs, but rather correlates all performance metrics across every layer of the big data stack, from the infrastructure to the services and applications, as well as within each layer to give a true full-stack view of application performance.
Unravel then applies artificial intelligence, machine learning, and predictive analytics to empower operations to optimize, troubleshoot and analyze from a single tool.
Optimize across applications, infrastructure and services
- Automatically speed up applications and improve resource usage efficiency
- Detect, diagnose and fix missed SLAs
- Automatically detect and eliminate rogue or resource wasting applications and users
- Get more out of storage – Remove “cold” tables, maximize tiered storage utilization
- Get more throughput by allocating the right size of pools and containers
- Setup auto-actions to keep applications and clusters optimized
Troubleshoot across applications, infrastructure and services
- “MRI” view of application performance – from code to infrastructure
- Automatically detect performance bottlenecks and failures
- Avoid or resolve contention issues
- Easily correlate infrastructure, services and application issues
- Setup auto-actions to resolve recurring issues automatically
Analyze, report and plan accurately
- Chargeback – accurate reporting for all usage on all clusters
- Cost savings – how to save costs by application, user, storage, CPU
- Auditing and governance – track usage and access to data and applications by users and groups
- Ops dashboard – bird’s eye view on health and performance of the entire stack
- 360° visibility – about applications, services, infrastructure and users
- Periodic reports – usage and access reports by users, applications, queues and clusters
Integrates seamlessly with all your Big Data systems
Unravel supports big data engines including Spark, MapReduce, Tez, Hive, Pig, Cascading, HBase, and Kafka, as well as workflow managers including Oozie, CRON, BMC and your home-grown solutions — in on-premise, cloud, or hybrid big data environments.
