Data + AI observability
Augmented data quality within your data lake and lakehouse – for a fraction of the cost.
Build reliable AI-ready data products
Full visibility. Total data confidence.
Scan every record across your pipeline to gain a holistic understanding of data health—no missed anomalies, no reconciliation gaps.
Predictable cloud costs.
Avoid unexpected cloud surges with an architecture purpose-built to minimize compute and storage overhead.
Ready for AI workloads.
Scale efficiently across large datasets with an open architecture that integrates natively with modern data stacks, including Apache Iceberg.
How it works
Observability for complex data architecture in 4 easy steps:
- Connect to any data lake or lakehouse with 250+ connectors.
- Monitor and analyze data health across your data estate.
- Triage alerts, tune data contracts, and augment DQ workflow with human-in-loop.
- Build reliable AI-ready data products.
Detect. Alert. Remediate.
Identify and fix data issues before they impact downstream apps.
Built for any data stack
Integrate data observability across your ecosystem without pre-processing and with native support for open table formats like Apache Iceberg, Delta Lake, and Hudi. With a dedicated compute layer and REST API design, you can easily scale your data pipelines.
Reliable data pipelines
Ensure your data is accurate, complete, and consistent across every stage of the pipeline, without creating bottlenecks. Automatically validate business rules, monitor for schema drift, and catch errors early—before they reach end users.
Surface critical outliers
Automatically detect unexpected changes in data values, distributions, and business metrics using machine learning. Surface outliers and changes across large datasets – even when patterns are subtle -to avoid schema drift and quality degradation.
End-to-end data integrity
Ensure data consistency and accuracy from ingestion to consumption. Monitor quality metrics, identify discrepancies, and use visual lineage to trace issues. Use root-cause analysis tools to quickly resolve problems before impacting reporting or AI models.
Data health at a glance
Gain clear visibility into your data’s health with interactive dashboards that display key quality metrics across your pipeline. Use a no-code interface to analyze data, track changes at the column level, and identify trends against historical baselines.
Resolve incidents faster
Proactively detect and triage data quality issues before they disrupt downstream applications or AI models. Intelligent alerting and root-cause analysis help teams act fast, reducing time-to-resolution and business impact.
Gain deeper visibility of your data health
Proactively monitor data pipelines across the distributed ecosystem
No-code connection to Data lake and lakehouse – natively support raw formats like Iceberg, Hudi, and Delta.
Validate every value before ingesting into AI-model, automate and orchestrate DQ workflows within AI-workloads.
No sampling, ML-driven anomaly detection on column-values and business metrics.
Improve quality across bronze, silver and gold layers embedded design patterns to stop bad data at bronze.
Learn MoreNo code analysis and reporting on data health metrics for your data lake and lake house.
Learn MoreAlerting, ticketing, investigation and remediation workflows.
Learn MoreML-driven data reliability
Ensure trustworthy data through active metadata and machine learning-powered insights.
Data metrics
Effortlessly track the data quality metrics that matter to your business.
Thresholds
Automated and manual thresholds help you spot issues before they escalate.
Notifications
Ensure that no anomaly goes unnoticed.
Investigations
Get to the root cause of data quality issues.
Security
With Actian Data Observability, your data is protected by industry-leading security.
Integrations
No-code, low-code integrations with over 250+ data sources.
FAQs
Observability measures how well a system’s internal states can be inferred from its external outputs. In software systems, observability involves making a system’s internal state visible through metrics, logging, and tracing. This helps developers and operators understand system behavior, diagnose issues, and improve performance.
Observability measures how well a system’s internal states can be inferred from its external outputs. In software systems, observability involves making a system’s internal state visible through metrics, logging, and tracing. This helps developers and operators understand system behavior, diagnose issues, and improve performance.
Data Observability is the practice of understanding and monitoring the behavior, quality, and performance of data as it flows through a system. It involves real-time tracking and analysis to ensure data reliability, accuracy, and compliance.
Key Aspects:
Data Quality: Accuracy, completeness, and consistency.
Data Flow: Movement through systems and identification of bottlenecks.
Data Dependencies: Relationships and impacts of changes.
Data Anomalies: Detection of outliers and errors.
Data Compliance: Adherence to regulations and policies.
Organizations achieve data observability using monitoring tools, data pipelines, quality checks, and governance practices. This helps them identify issues, make informed decisions, and maintain data integrity.
Data Observability is the practice of understanding and monitoring the behavior, quality, and performance of data as it flows through a system. It involves real-time tracking and analysis to ensure data reliability, accuracy, and compliance.
Key Aspects:
Data Quality: Accuracy, completeness, and consistency.
Data Flow: Movement through systems and identification of bottlenecks.
Data Dependencies: Relationships and impacts of changes.
Data Anomalies: Detection of outliers and errors.
Data Compliance: Adherence to regulations and policies.
Organizations achieve data observability using monitoring tools, data pipelines, quality checks, and governance practices. This helps them identify issues, make informed decisions, and maintain data integrity.
Actian Data Observability is an AI-powered enterprise data observability solution that provides complete visibility across your modern data stack. It proactively ensures data quality without sampling or cost surprises so you can confidently build reliable, AI-ready data products.
Our Differentiation:
No Data Sampling: We provide 100% data coverage for comprehensive and accurate observability, eliminating the blind spots and risks associated with sampled data.
No Cloud Cost Surge Guarantee: Our efficient architecture and processing ensure predictable, lower cloud costs for observability, unlike tools that can drastically increase compute/scan costs.
Secured Zero-Copy Architecture: We access metadata and run checks directly where data resides without creating insecure or costly data copies, leveraging existing security frameworks.
Scalable AI Workloads for Observability: Our Machine Learning (ML) capabilities are designed to scale efficiently on large enterprise datasets without requiring massive resource allocation.
Native Apache Iceberg Support: Deep, optimized integration provides unparalleled observability, specifically for organizations standardizing or migrating to Iceberg.
Actian Data Observability is an AI-powered enterprise data observability solution that provides complete visibility across your modern data stack. It proactively ensures data quality without sampling or cost surprises so you can confidently build reliable, AI-ready data products.
Our Differentiation:
No Data Sampling: We provide 100% data coverage for comprehensive and accurate observability, eliminating the blind spots and risks associated with sampled data.
No Cloud Cost Surge Guarantee: Our efficient architecture and processing ensure predictable, lower cloud costs for observability, unlike tools that can drastically increase compute/scan costs.
Secured Zero-Copy Architecture: We access metadata and run checks directly where data resides without creating insecure or costly data copies, leveraging existing security frameworks.
Scalable AI Workloads for Observability: Our Machine Learning (ML) capabilities are designed to scale efficiently on large enterprise datasets without requiring massive resource allocation.
Native Apache Iceberg Support: Deep, optimized integration provides unparalleled observability, specifically for organizations standardizing or migrating to Iceberg.
Data observability refers to the ability to understand and monitor the behavior of data within a system. It involves tracking data flow, detecting errors, and identifying discrepancies in real time, enabling early problem detection and system performance assessment.
With the integration of machine learning, data observability tools can intelligently monitor anomalies and business KPI drifts, offering deeper insights with lower maintenance and total cost of ownership (TCO).
Data quality, on the other hand, focuses on the accuracy, completeness, and consistency of data. It involves identifying and correcting errors, removing duplicates, and ensuring data is entered and stored consistently.
While both concepts provide visibility into data health and can detect quality issues against predefined metrics, data observability goes one step further by offering real-time monitoring and intelligent insights, enhancing overall system understanding and performance.
Data observability refers to the ability to understand and monitor the behavior of data within a system. It involves tracking data flow, detecting errors, and identifying discrepancies in real time, enabling early problem detection and system performance assessment.
With the integration of machine learning, data observability tools can intelligently monitor anomalies and business KPI drifts, offering deeper insights with lower maintenance and total cost of ownership (TCO).
Data quality, on the other hand, focuses on the accuracy, completeness, and consistency of data. It involves identifying and correcting errors, removing duplicates, and ensuring data is entered and stored consistently.
While both concepts provide visibility into data health and can detect quality issues against predefined metrics, data observability goes one step further by offering real-time monitoring and intelligent insights, enhancing overall system understanding and performance.
Unlock the Full Potential of Your Data + AI Initiatives
Ready to build reliable, AI-ready data products? Schedule a meeting with one of our data observability experts to:
- Understand how Actian Data Observability provides comprehensive visibility and ensures the health of your data pipelines.
- Explore the features that proactively detect issues and minimize business impact.
- Get insights into how to integrate data observability seamlessly with your existing data stack.
Ready to Dive Deeper? Request a personalized meeting today.