We are using power of Acceldata for our data quality issues. Its integration is simple and we are able to identify various data quality issues quite easily.
Timothy C.
Senior Expert of Data Governance - Enterprise
Acceldata has opened quite a few doors for us at Nestle. We have started with the Data Reliability features of the product and moved into the Cloud/Cost Optimization aspects.
Jayanth Reddy G.
Senior DevOps Engineer - Enterprise
This combination of usability, seamless integration, and robust features makes Acceldata a powerful tool for enhancing data reliability and performance.
Mark. H
Manager Data Warehouse Operations - Enterprise
Onboarding our data into Acceldata was very quick and simple. Within 24 hours all production data was loaded and trends were easily established.
Chidambararajan M.
IT Consultant - Enterprise
Acceldata is giving the better quality checks for the data sources and the maintenance of the environment is good.
Prashanth. S
Director, Platform Engineering and Data Operations - Enterprise
Acceldata has been instrumental in enhancing our data platform management capabilities. Their solutions not only ensure platform stability but also drive significant cost savings.
Leverage a rich set of analytics to simplify and automate data engineering tasks
Data Quality Automation:
Use data profiling, recommendations, and automation to quickly establish data quality policies without writing code.
Scale Data Reliability:
Leverage templates, tagging, and applying policies in bulk to scale out data reliability efforts and coverage.
Continuous Monitoring:
Use comprehensive dashboards, detailed insights, and real-time alerts to fix incidents swiftly and prevent future outages.
Predict and prevent data reliability and performance incidents
Monitoring:
Monitor and track data health to ensure issues are identified and addressed rapidly. Simplify root cause analysis with insights that correlate metrics across data, pipelines, infrastructure, and user layers.
Prediction:
Spot early warning signs of future incidents with trending analysis, allowing adjustments to be made before service levels are impacted.
Prevention:
Use workload analysis, recommendations, and simulations to ensure your platforms are always right-sized to meet requirements to prevent data issues.
Reduce and align costs with insights into resource utilization patterns and spend
Best Practices:
Simplify administration and establish consistent best practices with cost dashboards, utilization anomaly detection, and configuration recommendations.
Identification Inefficiencies:
Reduce costs without sacrificing performance with automatic detection of resource inefficiencies and other consumption anomalies.
Spend Confidently:
Plan and optimize spend by analyzing consumption in detail, monitoring usage, and placing guardrails to prevent runaway use and cost.
New to Data Observability?
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