A comparison of 12 open-source data observability tools (2026)

Inaccurate or poor-quality data causes artificial intelligence projects to fail without obvious warning.

Data quality usually declines gradually rather than failing suddenly. This drift often goes unnoticed until a dashboard displays incorrect information during a meeting or a model begins making decisions based on outdated data. At Shipshape Data, we consistently observe a specific pattern where data issues occur and remain undetected until after they have caused problems.

Open source data observability tools monitor data quality, pipeline status, and schema updates. These tools allow you to identify errors before they cause system outages or lead to incorrect business decisions. The market is divided into quality testing, cataloging and lineage, and infrastructure monitoring. Most established data stacks use a combination of tools from these different categories rather than relying on a single platform for every function.

Data observability tools address these issues. They monitor data quality, pipeline health, and schema changes. These tools provide alerts about changes before they cause system outages or lead to incorrect decisions. The open source options in this field have improved significantly. Users can now choose between small packages that integrate with dbt projects or comprehensive platforms that offer the same features as commercial software.

This is a review of twelve open-source data observability tools available in 2026. These tools solve different problems, so you should understand their specific functions before creating a shortlist. Some tools test data quality or track lineage. Others catalogue assets or monitor underlying infrastructure. This guide explains what each tool does, which users it suits, its included features, and its potential drawbacks. You should read the sections that apply to your technical stack and ignore the others.

1. OpenMetadata

OpenMetadata is a comprehensive open source option in this category. The platform combines metadata management, quality monitoring, and lineage. It is a practical choice for teams that need a single location to answer "what data do we have and is it healthy".

How it works

At the centre sits a metadata store behind a REST API. Connectors pull metadata from your databases, warehouses, pipelines, dashboards, and message queues into that store, and OpenMetadata builds a catalogue that tracks schema history, ownership, lineage, and quality in one view. You run it on Docker or Kubernetes, and it's built to scale sideways as your estate grows.

What you get in the box

• A searchable web catalogue that includes automated data profiling.
• Over 80 native connectors covering Snowflake, BigQuery, Airflow, Looker, and more
• A native quality framework used to define and execute tests through the user interface.
• Lineage visualisation and role-based access control

2. DataHub

DataHub was created at LinkedIn and is currently maintained by Acryl Data and a community of contributors. It uses a graph structure to store metadata. This design allows the system to track relationships between assets in large, distributed environments, which is difficult to do in relational databases.

How it works

All data exists in a generalised metadata graph. Ingestion sources add data through pull-based crawling or streaming via Kafka. This creates a system that updates as changes occur rather than waiting for a nightly scan. Lineage queries that would be too slow for a relational database run efficiently in this environment, even when they involve hundreds of connected datasets.

What you get in the box

• A searchable catalogue with automated lineage tracking
• Manage ownership, tagging, and a business glossary.
• Connectors for BigQuery, Redshift, dbt, and other common tools
• Real-time metadata propagation for teams using Kafka

The Kafka dependency is a significant requirement. This adds infrastructure overhead that smaller teams often avoid. The built-in quality testing features are limited, so you will likely need to integrate Great Expectations or Elementary to manage data quality. DataHub is appropriate for teams that already use Kafka and manage complex lineage across microservices and various platforms. The software is free under the Apache 2.0 licence, and a managed version is available through Acryl at commercial price points.

3. OpenLineage and Marquez

These two tools function together to achieve a specific purpose. OpenLineage is an open standard that establishes how pipelines send lineage events. Marquez is the reference backend used to collect and display those events. Using them together provides a vendor-neutral method for tracking data movement across pipelines without depending on a single platform.

How it works

OpenLineage defines a standard event format that pipeline tools send when a job completes. These events include information about the job's inputs, outputs, and run details, all in a consistent structure. Marquez receives these events via HTTP and uses them to create a searchable representation of your data flows. Integrations with OpenLineage are already available for Spark, dbt, and Airflow, which means implementing this functionality often requires minimal effort.

OpenLineage is a standard rather than a product. This means the lineage you record now remains portable even if you change your backend in the future.

Marquez is built to be a minimal tool. It lacks built-in quality testing, and its user interface is simpler than those in larger platforms. You must use a separate quality framework together with it. Within tools focused on lineage, this option is modular and lightweight. It suits teams using Airflow, Spark, or dbt that need lineage tracking without a full platform. Both tools are free to use under the Apache 2.0 licence.

4. Elementary

Elementary is a dbt-native tool. It operates inside your current dbt project rather than requiring a separate service.

Teams already using dbt will find this the easiest way to establish observability.

How it works

The software installs as a dbt package and runs within your existing dbt workflow. It writes run results, test outcomes, and schema changes into tables in your warehouse. You can view these details via a local report in a browser or upload them to a storage bucket for sharing. Since the data stays in your warehouse, you control retention, access, and query costs without external infrastructure.

What you get in the box

• Automated detection of anomalies in row counts, null rates, and data freshness.
• All your current dbt test results are gathered into a single structured report.
• Apply trend lines to track quality changes over time rather than concentrating solely on individual failures.
• A standalone service to deploy or maintain does not exist.

The tool's limitations are directly tied to its specific focus. Elementary does not work with pipelines that are not dbt. If you run Spark jobs or custom ingestion processes, you will need a different solution for those tasks. Lineage tracking is limited to the dbt graph instead of your whole data infrastructure. The software is free under the Apache 2.0 licence, and a paid cloud tier provides managed hosting and additional alerting features.

5. ODD Platform

ODD Platform is short for Open Data Discovery. It serves as a catalogue and observability platform built on the OpenDataDiscovery specification. This tool offers a structured way to collect, standardise, and display metadata from multiple sources. It eliminates the requirement to write manual ingestion code for each individual source.

How it works

It employs the OpenDataDiscovery specification to define how sources supply metadata. Collectors collect data from your databases, pipelines, and transformation tools and forward it to a central backend. This system builds a searchable catalogue and a lineage graph. Since the specification is standardised, metadata from various sources arrives in a consistent format. This cuts down the manual normalisation work your team would normally perform. The whole system is container-based for use with Kubernetes or Docker.

By default you receive catalogue and lineage, ownership management, and a business glossary, along with collectors for Snowflake, PostgreSQL, Airflow, and dbt. Built-in quality testing is minimal, so combine it with Great Expectations or Soda Core, and the collector ecosystem is smaller than OpenMetadata's or DataHub's, which may require building your own for niche sources. It fits mid-sized teams that need catalogue and lineage without adopting a heavier solution. Free under Apache 2.0.

6. Soda Core

Soda Core is a data quality framework that relies on YAML files. You are not required to write tests in Python or SQL. Instead, you generate check files to evaluate your data. This enables both analysts and engineers to handle data quality.

How it works

It connects directly to your warehouse or database and executes checks written in SodaCL, its YAML-based check language. You can run them from the command line or place them inside an orchestrator such as Airflow, and the outcomes are reported as pass, fail or warning. This makes it easy to incorporate into an automated testing pipeline with minimal setup.

What you get in the box

• Monitoring data freshness and row counts
• Identifying missing values and detecting duplicates
• Schema change detection
• Custom SQL-based checks, with support for Snowflake, BigQuery, Redshift, and PostgreSQL

SodaCL is readable enough for non-engineers to review and add checks without needing to know the underlying execution logic. However, Soda Core does not track lineage nor provide cataloging. If you require asset discovery, you must pair it with a separate metadata platform. This solution fits teams that need lightweight, code-defined checks in their CI/CD pipelines rather than a full platform. It is offered free under the Apache 2.0 licence, and a commercial managed cloud version is also available.

7. Great Expectations

Great Expectations is a widely used framework for data quality. You define declarative rules called expectations to specify the required characteristics of your data. You then validate these rules against your actual data to quickly spot errors.

How it works

The tool connects to sources using datasource objects and runs validation suites defined in Python or JSON. Each execution produces a results document that can be stored, shared, or used for alerts. Because it processes data in batches, you can run a suite on a specific dataset manually or as part of a scheduled pipeline. The system also automatically creates data docs, which are HTML reports that contain validation results and dataset statistics. These reports let stakeholders who do not write code review data quality.

What you get in the box

• A deep library of built-in expectations: nullness, uniqueness, value ranges, regex, referential integrity
• Data documentation that is automatically generated and easy for people to read.
• Version-controllable rules you can review in pull requests
• It serves as a validation step within Airflow or Prefect.

There's no native lineage or cataloguing, and the initial setup takes real configuration effort. Teams without Python will find the curve steeper than a YAML tool like Soda Core. Best for Python-centric teams that want precise, code-defined rules. Free under Apache 2.0, managed cloud at commercial tiers.

8. Deequ

Deequ is a quality library created by Amazon and built on Apache Spark. Unlike many other tools that query your data warehouse to execute checks, Deequ performs its checks inside the Spark computation layer. This method makes it appropriate for large batch workloads because it eliminates the cost of executing separate validation queries.

How it works

The tool computes quality metrics inside your current Spark job rather than doing a separate check later. You define constraints and metrics with Scala or Python, and Deequ executes them on the distributed dataset via Spark. It stores the results in a repository, enabling you to monitor how measurements evolve over time. The automatic constraint suggestion feature helps with new datasets that do not have quality documentation. It profiles the data and supplies a baseline for you to use.

The drawbacks are evident. The tool does not include a web user interface, data lineage, or cataloging features. Operating it effectively requires strong Spark expertise, which blocks teams lacking that experience. The tool suits teams that already employ Spark to handle large batch volumes and need validation within the pipeline. It is offered free of charge under the Apache 2.0 licence.

9. DQOps

DQOps is an abbreviation for Data Quality Operations. It merges a YAML check system, a web user interface, and a sensor-based architecture. The tool is built for teams that require scheduled quality monitoring and wish to avoid writing custom pipeline code for each individual check.

How it works

The tool connects via JDBC and runs checks on a predetermined schedule. You specify these checks in YAML files inside a DQOps workspace. The platform translates those definitions into SQL queries that execute against your data warehouse. The results are stored in a local time-series store. The user interface reads this data to show trends and detect anomalies over time. This arrangement lets a team create rules with the visual interface while keeping the configuration in a version control system.

What you get in the box

• A browser interface for managing checks, viewing results, and setting alert rules.
• We check that information is complete, correct, makes sense with other information, and is current.
• Support for Snowflake, BigQuery, Redshift and PostgreSQL
• The system has built-in anomaly detection which compares current data to historical baselines.

The software lacks native data lineage and cataloguing features. A separate tool is required if you need data discovery. Its connector library is smaller than that of older frameworks, which can lead to gaps when linking to less common data sources. The tool is best suited for teams that need scheduled visual quality monitoring instead of a full metadata platform. The software is free under the Apache 2.0 licence, and a commercial cloud version is also available.

10. OpenTelemetry

OpenTelemetry is distinct from most other tools on this list. It does not test data quality or catalogue assets. Rather, it offers a vendor-neutral method for collecting telemetry. You can use a single standard instrumentation layer to gather traces, metrics, and logs from your applications and pipelines.

How it works

This Cloud Native Computing Foundation project offers a standard collection of APIs, SDKs, and collector agents for instrumenting applications. You add the SDKs to your pipeline code. The collector then collects traces, metrics, and logs and forwards them to your chosen backend, for example Prometheus, Grafana, or a commercial platform; the export format remains vendor-neutral. This lets you switch your backend later without needing to rewrite the instrumentation in your code.

What you get in the box

• Language SDKs are provided for Python, Java, Go, and other languages.
• An independent collector which ingests, transforms, and exports data.
• Distributed traces follow a single event as it progresses through each stage of a pipeline.
• Determining the precise point where latency or failure occurs.

This tool does not carry out quality testing, data lineage, or cataloguing. It tracks system performance instead of data accuracy. You can mitigate this shortcoming by employing it together with a quality framework like Soda Core. It works best for teams that run custom pipeline code or microservices needing detailed operational monitoring. The software is free and is released under the Apache 2.0 licence.

11. Prometheus

Prometheus is a monitoring system for time-series data, created at SoundCloud and currently a graduated CNCF project.

This software does not qualify as a standard data quality tool. It works together with OpenTelemetry and Grafana as a primary component of the infrastructure monitoring layer. Many data teams employ it to monitor the health of their pipelines.

How it works

The system employs a pull model to gather metrics by scraping HTTP endpoints on a regular schedule. These endpoints deliver data in a standard text format. The metrics are kept in a local time-series database. Trends can be analysed and alerts configured using PromQL, the system's dedicated query language. It handles high-cardinality data efficiently. This makes the tool dependable for monitoring pipelines that produce many distinct metric labels.

What you get in the box

• Alertmanager is an integrated engine that handles alerts.
• It includes a simple web interface for executing manual PromQL queries.
• We supply exporters for PostgreSQL, Kafka, and Airflow, enabling most teams to add instrumentation without building custom endpoints.
• It provides consistent operational metrics for latency, job failure rates, and queue depth.

The tool does not offer quality testing, data lineage, or cataloging features. Long-term data retention needs a remote store such as Thanos or Cortex, which adds system complexity. It works best when you need reliable operational metrics and a proven alerting system. The software is available free under the Apache 2.0 licence.

12. Grafana

Grafana is the visualisation tool most teams use with Prometheus, OpenTelemetry, or other metric backends. The software itself does not collect or store data. It transforms raw telemetry into dashboards. These dashboards let engineers and stakeholders watch pipeline health without writing manual queries.

How it works

It links to your existing sources via a plugin system and queries them directly. You configure data source connections to Prometheus, ClickHouse, PostgreSQL, or any other system you run, and Grafana retrieves real-time and historical data on demand as dashboards load. The rendering engine produces time-series graphs, heatmaps, stat panels, and alert views from a single interface.

What you get in the box

• A tool for constructing dashboards by dragging and dropping components.
• We offer many community dashboard templates, including versions for Airflow and Kafka.
• Built-in alerts support Slack, PagerDuty, or email.
• There are more than 150 data source plugins.

Pre-built templates let most teams achieve clear pipeline visibility within a few hours. This tool does not provide quality testing, data lineage, or cataloguing features. Grafana shows only the data you instrument, so dashboards will be incomplete if your metric coverage is low. The open source edition uses the AGPLv3 licence rather than Apache 2.0. You should check your internal distribution policies for this licence before adopting it. The software can be self-hosted for free, while the managed cloud version requires a commercial subscription.

How to make a choice

Twelve tools exist, and there is no single winner because they answer different questions. Roughly, they fall into three groups. Quality testing: Soda Core, Great Expectations, Deequ, DQOps, and Elementary. Cataloguing and lineage: OpenMetadata, DataHub, ODD Platform, OpenLineage with Marquez. Infrastructure monitoring: OpenTelemetry, Prometheus, and Grafana. The most mature stacks typically run two or three of these, rather than relying on one to cover everything.

Start by addressing your most significant deficiency instead of prioritising a comprehensive feature list. If you use dbt, Elementary gives the fastest route to reliable monitoring with minimal configuration. For large-scale governance and discovery needs, begin with OpenMetadata or DataHub. If your work depends heavily on Spark batch processing, give priority to Deequ. If you need transferable lineage information that avoids vendor lock-in, choose OpenLineage with Marquez. Finally, if the main problem is a lack of visibility into pipeline execution, implement OpenTelemetry, Prometheus, and Grafana as a foundational operational setup.

One warning worth repeating. Picking a tool before you understand your own data architecture is how teams end up with coverage gaps that only surface during an audit or an outage, which are the two worst moments to find them. We've walked into this more than once: a shiny observability platform installed, and the one pipeline that keeps breaking sitting outside everything it watches. Map your estate first, then buy for the gaps.

If you are trying to move AI work from pilot into production and are unsure which of these fits your stack, that is the kind of problem we help teams untangle at Shipshape Data. Talk to us and start with a clear read on where your data actually stands.

Observability is only effective when the underlying system functions correctly. If your pipelines need more than simple monitoring, our data consultancy can help you resolve the root causes instead of tracking the symptoms.

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