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For Data Engineers

Improve data quality, reliability, and performance

Data observability empowers data practitioners with insights to improve data quality, reliability, performance, and efficiency.

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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

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.
Spot early warning signs of future incidents with trending analysis, allowing adjustments to be made before service levels are impacted.
Use workload analysis, recommendations, and simulations to ensure your platforms are always right-sized to meet requirements to prevent data issues.

New to Data Observability?

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PhonePe uses Acceldata to scale open-source data platform by 10x

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