CheckOps
Soda Core
Soda Core is a data quality and data contract verification engine that defines contracts in YAML and validates schema and data across PostgreSQL, Snowflake, BigQuery, Databricks, DuckDB and more; the source is available under the Elastic License 2.0.
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Introduction
Soda Core is a data quality and data contract verification engine. It lets you define data quality contracts in YAML and automatically validate both schema and data across your data stack, according to its README.
The Soda CLI and Python API generate, test, publish and verify contracts. Checks can run locally during development, inside pipelines orchestrated by Airflow, Dagster or Prefect, or remotely when connected to Soda Cloud. The README highlights more than 50 built-in data quality checks and support for PostgreSQL, Snowflake, BigQuery, Databricks, DuckDB and other sources, while Soda Cloud adds centralised management and anomaly detection.
The licence is the Elastic License 2.0, which is source-available rather than OSI open source. Release v4.30.0 shipped in November 2026.
In the quality loop it supports data contracts in pipelines and production monitoring.
Features
- YAML contracts: Readable data contracts.
- 50+ checks: Built-in validations.
- Many sources: Warehouses and databases.
- Pipeline integration: Airflow, Dagster, Prefect.
- Soda Cloud: Optional central management.
Pros
- Simple syntax: Easy for analysts.
Cons
- Elastic License 2.0: Not OSI open source.
- Cloud features paid: Advanced monitoring needs Soda Cloud.
Usage and Links (Example)
pip install soda-postgres
soda contract verify -ds ds_config.yml -c contract.yml
Resources:
Pricing
Soda Core free under Elastic License 2.0; Soda Cloud plans. See site.
Recommended for?
- Data teams adopting data contracts.
- Not a fit if you require an OSI licence.
Conclusion
Soda Core is a practical, YAML-first data contract engine.
On CheckOps, Soda Core is filed under the Integration testing and Production monitoring & observability stages. Related tools listed here include Great Expectations (GX), Deequ, Pandera, pytest, Hypothesis and Giskard. Before choosing, compare them on licence, supported languages and platforms, hosting model, and how well each fits your existing CI pipeline. Facts on this page were checked against the official website, documentation and repository on 11 November 2026; statements the vendor makes about its own product are marked as vendor claims.
Alternatives
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DDeequDeequ is an Apache-2.0 library from AWS Labs built on Apache Spark for defining 'unit tests for data' that measure data quality in large datasets, with constraint suggestions, metrics and anomaly detection; PyDeequ provides a Python interface.Compare with Soda Core
PPanderaPandera is an MIT-licensed Python framework for dataset validation that defines schemas for pandas, Polars, PySpark and other dataframes and validates types, values and statistical properties.Compare with Soda Core- Ppytestpytest is the usual Python test runner: plain asserts, fixtures, parametrization, and a large plugin set.Compare with Soda Core
HHypothesisHypothesis is the MPL-2.0 property-based testing library for Python: describe the range of inputs, let it generate cases including edge cases, and get the simplest failing example when a property breaks.Compare with Soda Core
GGiskardGiskard offers an Apache-2.0 open-source library for evals, red teaming and test generation for AI agents, plus a commercial Hub for agent red-teaming, evaluation and guardrails.Compare with Soda Core