Automate data quality metric monitoring
Ensure the gold layer is “consumption-ready” data by identifying and resolving data issues before they flow through the silver and gold layer.
Ensure data reliability with dependency monitoring
Actian Data Observability allows users to visualize dependencies between monitored data sources to help with troubleshooting data issues by displaying dependency graph with number of detected issues at each node of the graph.
Monitor data health across all data layers
- Get a complete overview of the health status of all systems within your pipeline.
- Visualize lineage to monitor data issues and KPIs through your pipeline.
- View alerts from Actian Data Observability when inconsistencies are detected between the silver, bronze, and gold layer.
Track data from ingestion to consumption
- Data inconsistencies across its lineage can be an early indicator of potential problems. Actian Data Observability allows you to detect and assess issues between systems before they escalate.
Quickly find the source of issues
- Actian Data Observability searches across your entire data pipeline to find the source when inconsistencies occur.
- Our UI-based investigator allows easy root-cause analysis.
- Faster mean time to resolve, issue triaging, and resolution.
Connect. Analyze. Alert. Advise.
Connect Datasources
Connect your datasource, or send data via REST, or load a local file.
Analyze Data Health
Quickly identify and pinpoint data anomalies, errors, or inconsistencies.
Alert
Actian will learn your data and its trends and automatically alert on unexpected drifts.
Recommendations
Actian will finally advice you on next best actions for your data sets.
Discover the complete platform
Open architecture
No-code connection to data lake and lakehouse –natively supports raw formats like Iceberg, Hudi, and Delta.
Data quality
Validate every value before ingesting into AI-model, automate and orchestrate DQ workflows in AI-workloads.
Anomaly detection
No sampling, ML-driven anomaly detection on column values and business metrics.
Data layer consistency
Improve quality across bronze, silver, and gold layers embedded design patterns to stop bad data at bronze.
Data layer health
No code analysis and reporting of your data lake and lake house.
Incident management
Alerting, ticketing, investigation and remediation workflows.
