Data Engineering Productivity
Design: Avoid costly refactoring by ensuring solutions are optimized from the start for performance, quality, and cost. Data engineers can compare metrics for different data sets and technologies to determine the best design.
Development & Deployment: Data engineers can automate data reliability by identifying highly-used data assets and data pipelines and applying monitoring at scale for agile rollouts.
Continuous Monitoring: Data teams can eliminate bottlenecks and fix data incidents before they become serious issues through continuous data reliability monitoring and proactive identification of root causes.
Monitoring: Track data pipeline health with anomaly detection and timeliness monitoring to ensure data incidents are addressed rapidly and data reliability service levels are met.
Prediction: Identify data incidents and their root cause to prevent future issues with multi-layer data and trend analysis.
Prevention: Use resource utilization analysis and configuration recommendations to make your platforms are always right-sized and meet requirements to prevent data issues.
Best Practices: Use insights and recommendations to implement best practices that establish efficient and performant systems.
Identification of Waste: Reduce costs without sacrificing performance by identifying resource inefficiencies and utilizing configuration recommendations.
Spend Effectively: Budget owners can confidently plan and optimize spend by analyzing consumption with granular detail and place guardrails to prevent runaway utilization.
"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."
"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.”
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