Overview

Acceldata Pulse provides real-time intelligence across all your observability needs. You can build your workflows and extend alerts for business requirements with algorithmic anomaly detection. Acceldata Pulse also provides recommendations and automation to keep your data systems performant, secure, and reliable.

Pulse integrates with all systems, subsystems, and services to provide you with insights to run your big data infrastructure. Pulse provides reliability, monitoring, efficiency, recommendations, and root cause analysis.

Pulse provides you with comprehensive, real-time, and actionable intelligence for your data observability needs. Observability is a measure of the internal state of a system using the external outputs. Pulse provides integrated visibility into data for Developers, Data Scientists, and Operations.

Acceldata Pulse helps data teams proactively monitor, analyze, and optimize big data platforms.

Key benefits

• Troubleshoot slow queries faster

• Plan and optimize resource usage

• Detect issues before they impact users

• Receive intelligent performance recommendations

• Manage alerts and automatically remediate problems

• Search and analyze records and logs across the stack

• Proactively detect anomalies and potential issues before failures occur

Pulse Core Capabilities

Monitor and Analyze

• End-to-end visibility across the Hadoop and big data ecosystem

• Monitor and analyze hundreds of jobs to identify performance outliers

• Troubleshoot slow queries faster using full application history

• Debug applications efficiently with a complete application history

• Search and analyze records, metrics, and logs across the entire stack

• Detect anomalies and identify issues proactively before impacting users

Manage Alerts and Actions

• Generate intelligent alerts based on anomalies and thresholds

• Detect issues early and notify teams before failures occur

• Reduce MTTR from hours to minutes with advanced root cause analysis

• Correlate errors across services and applications

• Automatically remediate common operational issues

• Centralized alert management across all data systems

• Create forecasting alerts that predict future metric deviations using trained models and notify users up to an hour in advance before thresholds are breached.

Optimize Resources

• Optimize YARN CPU and memory utilization across the cluster

• Identify inefficient workloads and resource bottlenecks

• Reduce job wait times and improve workload concurrency

• Receive intelligent, system-specific optimization recommendations

• Improve cluster efficiency and stability without adding hardware

• Enable data-driven capacity planning using real utilization insights