dbt Tests vs Great Expectations vs Soda: A Comprehensive Comparison

When deciding between dbt tests, Great Expectations, and Soda for data quality management, it's essential to understand each tool's strengths and limitations. Per the dbt documentation, dbt tests live inside your dbt project and run with it. Great Expectations offers a flexible, open-source Python framework for data validation (GX Core; GX Cloud was acquired by FICO and has not been publicly available since June 1, 2026). Soda pairs its Soda Core library with Soda Cloud for data contracts, monitoring and anomaly detection.

Key Takeaways

• •dbt tests (data tests and unit tests) are ideal for teams already in the dbt ecosystem: they are written in SQL and YAML and run with dbt test or dbt build.
• •Great Expectations (GX Core) is an open-source Python framework under Apache 2.0, stewarded by Fivetran since 2026; GX Cloud is no longer publicly available (from June 1, 2026).
• •Soda combines Soda Core, a Python library and CLI under the Elastic License 2.0, with Soda Cloud for collaborative data contracts, observability and record-level anomaly detection, run on a Soda-hosted or self-hosted runner.

dbt Tests Overview

dbt tests are embedded within the dbt project. Singular data tests are SQL queries that return failing rows; generic data tests (unique, not_null, accepted_values, relationships, or your own) are parameterized queries applied to models in YAML; unit tests check model logic against fixed inputs. This integration is beneficial for teams already using dbt, as it streamlines testing within the existing workflow, making it a powerful tool for SQL-savvy teams.

Beyond the basic integration, dbt tests provide a structured way to manage data quality by embedding tests directly into the transformation layer. This means that data quality checks are part of the same process that transforms data, reducing the need for separate validation steps. The SQL-based nature of dbt tests ensures that users familiar with SQL can easily write and manage tests, leveraging existing skills.

However, dbt tests have limitations, particularly in their real-time monitoring capabilities. While they are excellent for validating transformations as they occur, they do not inherently provide real-time anomaly detection or monitoring. This makes dbt tests less suitable for environments where immediate feedback on data quality is necessary.

One of the key advantages of dbt tests is their ability to integrate with existing data workflows without introducing additional complexity. For teams already committed to the dbt ecosystem, using dbt tests can minimize the learning curve and ensure that data quality is maintained as part of the transformation process. However, the reliance on SQL may be a barrier for teams less familiar with SQL scripting, potentially requiring additional training or resources.

Great Expectations Overview

Great Expectations is an open-source framework designed to provide data validation and documentation. It allows users to create 'expectations' or assertions about data, which can be used to validate data pipelines. GX Core, the Python library, is Apache 2.0 and now developed under Fivetran's stewardship (its repository moved to github.com/fivetran/great_expectations). Its flexibility and comprehensive documentation make it a popular choice for teams looking for a customizable solution.

The strength of Great Expectations lies in its ability to define complex data validation rules, known as 'expectations,' in Python. This flexibility allows teams to tailor validations to their specific needs, accommodating a wide range of data types and structures. The framework's open-source nature means it can be integrated into various data environments, supporting diverse workflows.

While Great Expectations excels in flexibility, it does not focus on real-time monitoring. The framework is more suited for batch validation processes, where data is checked at specific intervals. This makes it ideal for organizations that need thorough validation but can afford to wait for batch processing.

Great Expectations also offers extensive community support and resources, which can be a significant advantage for teams looking to implement a flexible, open-source solution. Teams that relied on GX Cloud for hosted results and alerting need a new home for them: per the GX announcement of May 6, 2026, GX Cloud went to FICO and stopped being publicly available on June 1, 2026. Scripting validations in Python allows high customization but may require more technical expertise than other tools.

Soda Overview

Soda splits the work between Soda Core, which runs data contracts in your pipelines, and Soda Cloud, which adds monitoring, anomaly detection, collaborative contracts and AI assistance. Checks run on a Soda-hosted runner or on a self-hosted runner inside your own environment. Soda's interface and automated alerts make it suitable for organizations prioritizing continuous data quality monitoring.

Soda's strength is its focus on continuous monitoring. Observability dashboards with adaptive thresholds and record-level anomaly detection flag data issues as data lands, and data contracts give business and engineering a shared, auditable definition of good data. This is crucial for businesses that rely on up-to-date data for decision-making.

For strict data residency, Soda's self-hosted runner executes checks inside your environment, and Soda Core runs wherever your pipelines run. Soda's AI features are assistive: per Soda's documentation, every proposed action is shown and approved by a person before it is applied.

Soda's emphasis on contracts and anomaly detection positions it as a strong candidate for organizations that need immediate insights into data health. Plain-English check writing and automated features reduce the need for extensive technical expertise, making it accessible to a broader range of users. However, potential users should note that Soda Core's Elastic License 2.0 (since its v4 release in January 2026) is source-available rather than OSI open source, and the collaboration features need Soda Cloud.

Comparison Table

Frequently Asked Questions

What are the primary differences between dbt tests and Great Expectations? dbt tests are SQL-based and integrated into the dbt project, making them ideal for teams already using dbt. Great Expectations, on the other hand, provides a standalone, flexible framework for data validation using Python.

How does Soda compare to Great Expectations in terms of monitoring? Soda Cloud monitors continuously, with anomaly detection, adaptive thresholds and automated alerts. GX Core validates in batches when your pipeline runs it, and with GX Cloud no longer publicly available, hosted monitoring for GX is now up to you. Soda is the better choice for organizations needing immediate insights.

Which tool is best suited for a team already using dbt? dbt tests are the most suitable for teams already using dbt, as they offer seamless integration and SQL-based testing within the existing dbt workflow.

Can Great Expectations be integrated with other data tools? Yes, GX Core integrates with orchestrators such as Airflow, warehouses and other data platforms, offering flexibility in deployment and customization for different environments.

How does the deployment choice affect the suitability of each tool? Open-source dbt runs wherever you run it (a laptop, CI or an orchestrator), while the dbt platform (formerly dbt Cloud) is SaaS run by dbt Labs. GX Core is self-managed in your own environment. Soda Core runs in your pipelines, and Soda Cloud can execute checks on a self-hosted runner, which suits organizations with stringent data residency requirements.

Data Workers, the agentic data platform, works alongside all three. When a failure reaches it (a Soda result, a failed Airflow task, or a dbt test failure your team hands over), it traces the failure upstream through dbt lineage, re-checks the affected tables on Snowflake, Postgres or BigQuery (nulls, key uniqueness, row counts), flags drift against the baselines your team records, and proposes the fix as a diff for the owner to merge, with its blast radius. Soda is a native connector: Data Workers reads Soda Cloud check results and monitors, and triggers a check run to re-verify after a fix. For GX Core, a failed Checkpoint fails its Airflow task, and Data Workers reads that run through its Airflow connector. See our field guides for dbt, Great Expectations and Soda, and the Atlan alternatives comparison for the governance side.