Data Observability for Hadoop
For enterprises running Hadoop, Acceldata Pulse delivers improved reliability, performance, and efficiency of data processing at scale.
Acceldata Pulse improves the reliability, performance and efficiency of Hadoop
Predict & Prevent Incidents
Most customers have achieved a 90%+ reduction in sev 1 incidents and some are yet to have a single sev 1 incident since deploying Pulse
Performance Incidents
Pulse’s advanced performance monitoring & analytics has enabled customers to easily identify & eliminate bottlenecks to accelerate development and scale in production to keep pace with rapid growth and demand
Insights into resource utilization and efficiency with corresponding optimization recommendations has enabled many customers to meet business requirements with 30% lower infrastructure costs
Costs
Transition from reactive incident response to proactive incident prevention
Prediction: Trending analysis provides early warning indicators of future failures and delays, such as increases in runtime, resource consumption, and other metrics. This allows engineers to make adjustments before service levels are impacted.
Prevention: An extensible, Ansible-based auto-action framework, with dozens of out-of-the-box runbooks, enable systems to be self-tuning and self-healing. A rich and flexible alerting and notification framework engages engineers at the right time, every time.
Resolution: Accelerate root cause analysis with event correlation across infrastructure, platform services, processing and data layers. Deep analytics provides insights based on historical comparisons, environment health, resource contention, and other metrics.
Simplify development, tuning and scaling of data processing workloads
Recommendations: Pulse automatically generates performance optimization recommendations for Spark jobs, Hive queries and other workloads.
Simulation: Easily right-size Spark job configuration to meet objectives such as runtime, resource, or performance metrics
Analysis: Gain insight into bottlenecks, excessive overhead, and execution plans, and other factors to optimize code and queries for performance and efficiency
Meet service level objectives at a lower cost
Capacity: Utilization insights to improve planning, chargebacks, scheduling, and hybrid cloud management ensure infrastructure spend aligns with business priorities and benefits
Data Processing: Pulse automatically flags inefficiencies across the entire environment with drill-down into performance analytics and recommendations
Data Engineering: Pulse provides deep visibility and insights into data usage, distribution, hotspots, small files and other factors that can affect performance and efficiency
Acceldata Pulse provides the most comprehensive observability platform for the Hadoop ecosystem
Burzin Engineer, Founder & Chief Reliability Officer, PhonePe
"Acceldata supports our hyper-growth and helps us manage one of the world’s largest instant payment systems. PhonePe’s biggest-ever data infrastructure initiative would never have been possible without Acceldata."
Ashwin Prakash, PubMatic Data Analytics Engineering Leader
"Acceldata provided the data observability tools and expertise to improve our data pipeline reliability. They helped us optimize HDFS performance, consolidate Kafka clusters, and reduce cost per ad impression, which is one of the most critical performance metrics.”
Wanlapa Linlawan, Head of Analytics Platform, True Corporation
"Acceldata’s tools fixed our analytics pipeline issues, improved visibility into our data systems, and recommended ways to scale and optimize our systems to meet future requirements. They helped True Corporation transition to open-source technologies, allowing us to reduce licensing costs, while delivering mission-critical analytics across the enterprise.”
Related Resources
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