Design: Optimize solution design by analyzing designs and workload impact across your entire data stack to maximize quality, performance, and cost metrics.
Scale Data Reliability: Leverage templates, tagging, segmentation, and applying poliOptimize Deployment: Simplify tuning for scale with bottleneck analysis, configuration recommendations, and a simulator to ensure performance meets requirements.
Post-Deployment Data Management: Use real-time insights and alerts to monitor ever-changing workloads and leverage recommendations to tweak configurations on demand.
Monitoring: Track your data platform's operational health, get real-time alerts on incidents, and use multi-layer data to simplify root cause analysis.
Prediction: Spot early warning signs of future incidents with trend analysis, allowing adjustments to be made before service levels are impacted.
Recommendations: Use configuration recommendations and simulations to right-size deployments for current and future workloads based on performance and cost.
Best Practices: Establish best practices for resource consumption and optimization and use configuration recommendations to maintain these.
Eliminate Waste: Reduce costs without sacrificing performance with automated resource inefficiency detection and recommendations.
Optimize Spend: Efficiently plan and optimize spend by monitoring usage, analyzing consumption, and placing guardrails to prevent runaway use.
"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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