Open Source Data Observability: Building Trusted, AI‑Ready Pipelines

In the era of AI-first strategies and data-driven decision-making, the importance of observing and ensuring the health of your data pipelines can’t be overstated. Open source data observability has emerged as an essential paradigm, offering transparency, flexibility, and community innovation to monitor data quality, lineage, and schema drift.

However, open source alone may lack the enterprise-grade scalability, security, and integration necessary for modern data stacks. That’s where Actian steps in. It combines open source tools with the powerful Actian Data Intelligence Platform to deliver fully observable, AI-ready data pipelines.

Why Open Source Data Observability Matters

Open source data observability refers to the practice of instrumenting and understanding the health of data pipelines using community-driven solutions. These tools enable teams to detect anomalies, track data freshness, and monitor schema changes, all through transparent, inspectable code. Key benefits include:

• Transparency and control: You can inspect every metric and validation rule.
• Flexibility: Customize pipelines to your specific needs without vendor lock-in.
• Community‑driven evolution: Benefit from ongoing updates across the open source ecosystem.
• Cost efficiency: Many tools are free or open core, reducing licensing fees.

Also, keep in mind that with greater visibility comes complexity. Managing multiple tools like Great Expectations, OpenMetadata, Prometheus, and Grafana can quickly become overwhelming, especially as data volumes scale and governance demands grow.

Limitations of Pure Open Source

While open source observability tools excel in modularity and transparency, they also have some drawbacks:

• Scalability challenges: Scaling validation or lineage tracking across petabyte-scale lakehouses requires significant engineering muscle.
• Cloud cost volatility: Running full data scans often leads to unpredictable compute charges.
• Security gaps: Moving or copying data between systems may introduce compliance and data privacy issues.
• Complex integration overhead: Stitching together open source pipelines with metadata, lineage, monitoring, and alerting demands ongoing maintenance.
• Copy inefficiencies: Many open source tools copy data for validation, leading to latency and redundancy.

These are exactly the gaps that the Actian Data Intelligence Platform is designed to address.

How Actian Enhances Open Source Observability

Actian Data Intelligence Platform—and in particular the Actian Data Observability solution—bridges open source gaps with enterprise-grade capabilities:

Full Coverage, No Sampling

Unlike many open source tools, Actian Data Observability offers 100% data coverage across your data estate—including data lakehouses, warehouses, and Iceberg/Delta/Hudi tables—without sampling. No metric is missed.

Predictable Cloud Economics

Actian’s zero-copy, in-place model runs scans in a dedicated layer, ensuring controlled compute usage. The result is stable cloud costs without surprise bills.

Security-First Architecture

Actian connects directly to your data sources to extract metadata and run checks. Your raw data never leaves its system—enhancing compliance and data privacy.

ML-Driven Anomaly Detection

Powered by AI/ML, Actian automatically surfaces outliers, schema drift, and performance anomalies across massive datasets. It also provides root-cause analysis and offers suggestions to accelerate remediation.

Modern Data Format Support

With native Apache Iceberg support, Actian is purpose-built for emerging data lakehouse formats, fully observing both data and metadata.

Seamless Open Source Integration

Actian Data Observability complements—not replaces—open source. Use it alongside:

• Great Expectations for data quality tests.
• OpenMetadata for cataloging and lineage.
• Prometheus + Grafana for infrastructure metrics.
• Airflow or dbt for orchestrated pipelines and quality enforcement.

This unified approach retains flexibility while ensuring scaling reliability.

5 Open Source Observability Tools to Get Started

These open source tools can be used for data observability, while Actian complements and expands their capabilities:

1. Great Expectations

A Python-first framework for defining “expectations.” Easily integrates into extract, transform, and load (ETL) pipelines to test freshness, value ranges, and schema compliance. Use Actian to validate metrics post-ingestion and run advanced anomaly detection.

2. OpenMetadata / DataHub

These metadata-first platforms offer data lineage, cataloging, and governance. Let Actian connect to that metadata to layer ML-powered observability on top.

3. Prometheus + Grafana

This is the de facto standard for monitoring infrastructure. Actian complements it by monitoring the data flow, not just the platform.

4. dbt + Airflow / Prefect / Dagster

Use dbt for data transformation and testing, and pair it with orchestration tools like Airflow, Prefect or Dagster for flow control. Augment the stack with Actian Data Observability to enable alerting and deep analysis.

5. Apache Iceberg Tools

This offers native open table formats with metadata insights. Actian’s deep Iceberg integration brings visibility to the ecosystem and adds anomaly detection and cost control layers.

A Sample Workflow: Observability in Action

This six-step process shows how data observability works in a traditional workflow:

1. Ingestion

• Load raw data into Iceberg tables via open source ingestion tools.
• Use Great Expectations macros to validate schemas and null counts.

2.  Transformation

• dbt processes and writes to bronze/silver/gold layers.
• Integrate open source data quality tests into CI/CD.

3.  Cataloging and Lineage

• OpenMetadata automatically ingests schema, lineage, and tags.
• Actian taps into the data catalog to define monitoring scopes.

4.  Observability Overlay

• Actian runs ML-powered scans over transformed data and lineage metadata to detect anomalies, drift, and cost fluctuations.

5.  Alerting and Resolution

• Actian raises alerts in its user interface. Optional alerts can be sent via Slack or PagerDuty.
• Actian provides root-cause insights, such as: “schema change in orders table triggered null spike downstream.”

6.  Feedback Loop

• Engineers resolve the root issue; observability alerts adjust thresholds.
• New metrics tracked via Actian; dashboards updated.

Why Actian Offers the Ideal Enterprise Tier

• Scalable and performant: Handles parallel scans of thousands of tables without slowing pipelines.
• No-surprise billing: Guaranteed cloud usage without scan surges.
• Secure and compliant: Metadata-only architecture; SOC 2 and ISO 27001 certified.
• Iceberg native: Built for next-generation data lakehouse formats.
• Integrated data intelligence: Anomaly detection, lineage, catalogs, marketplace, and governance in one platform.

The Bigger Picture: Data Intelligence

Data observability isn’t an endpoint. It’s part of a broader data intelligence approach. Actian Data Intelligence Platform brings it all together:

• Data catalog and data marketplace in a unified platform
• Active metadata management, data contracts, and governance by design
• Observability and quality assurance, augmented with AI/ML

This unified platform ensures data is discoverable, trustworthy, governed, and highly observable, making it AI-ready.

Get Started With Actian and Open Source

Follow these five steps to launch your data observability solution:

1. Explore open source tools. This can include Great Expectations, OpenMetadata, and Prometheus.
2. Map your observability needs, such as data quality, freshness, lineage, and anomaly detection.
3. Pilot Actian Data Observability on a critical pipeline.
4. Analyze anomalies and costs, and compare with open source alone.
5. Scale up, embedding Actian across your production pipelines, while continuing to use open source for specific tasks.

Why Observability Matters Now

• Gartner projects that by 2026, half of enterprises using distributed data architectures will adopt observability tools.
• AI pipelines of increasing complexity demand full visibility.
• Cloud billing unpredictability can derail budgets.
• Enterprise compliance requires zero‑copy, secure data workflows.

Actian’s model ensures teams maintain the openness and flexibility of community tools while achieving enterprise-grade reliability, security, and cost control.

Optimize Open Source Data Observability With Actian

The fusion of open source data observability tools with Actian Data Observability delivers a powerful synergy. You gain inspection capabilities via customizable community tools, plus enterprise scalability, security, and intelligence.

With open formats like Apache Iceberg at the core, and full integration across data cataloging, contracts, quality, and observability, Actian accelerates your ability to build AI-ready data products efficiently, confidently, and cost-effectively.

Start your journey today. Explore open source tools, experience Actian Data Observability in action, and discover how full-stack data intelligence empowers your teams to trust their data at scale.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.