Amazon Deequ vs Soda
Amazon Deequ
github.com
Soda
soda.ioSide by side
| Figure | Amazon Deequ | Soda |
|---|---|---|
| Price from | Free | $750/mo |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 3 |
| Spec score · Database Testing Tools | 8.7 (#3) | 8.7 (#4) |
| Spec score · Data quality testing tools | 8.7 (#1) | 8.5 (#2) |
| Integrations | Deequ is built on Apache Spark, is distributed through Maven artifacts, and has a Python interface called PyDeequ. | Soda advertises integrations with data-stack tools, catalogs, orchestration systems, incident-management tools and CI/CD workflows. |
| License | The library is licensed under the Apache 2.0 License. | Soda Core changed from Apache 2.0 to Elastic License 2.0 in January 2026; the source remains readable and modifiable but cannot be offered as a competing hosted service. |
| Purpose | Deequ is an Apache Spark library for defining unit tests that measure data quality in large datasets. | Soda is a data quality platform for monitoring data quality, catching problems early, understanding issues at source, and taking action. |
Plans
In their own words
Amazon Deequ
Amazon Deequ is an Apache Spark library for writing data unit tests that measure quality in large datasets, including datasets with billions of rows. Its checks cover row counts, completeness, uniqueness, allowed values, nonnegative values, patterns, and approximate quantiles. Deequ also supports data profiling, stored and queryable metrics, anomaly detection over time, automatic constraint suggestions, and incremental metric computation. Its declarative Data Quality Definition Language includes rules for counts, completeness, uniqueness, statistics, schema matching, freshness, and custom SQL. Row-level evaluation can identify records that pass or fail supported rules, while dataset-level rules such as RowCount and Mean are skipped in that mode. Deequ targets specific Apache Spark versions, so the release must match the Spark version; versions 2.1.0 and later require Java 11. The project provides Maven and sbt dependency examples. It is licensed under Apache 2.0, and Python users can use the PyDeequ interface.
Soda
Soda is a data quality platform for monitoring production data, testing data through development and deployment, and investigating issues. Its machine-learning-powered observability looks for unexpected changes without requiring every rule to be set in advance. Teams can test data during development, deployment, transformation, and CI/CD workflows. Data contracts let producers and consumers specify expected schemas, data types, value ranges, and other constraints. Soda lists integrations including Databricks, Snowflake, PostgreSQL, Airflow, dbt, Slack, Microsoft Teams, Jira, and PagerDuty, and offers three deployment models for different infrastructure and privacy needs. Its listed database support includes Athena, BigQuery, Databricks SQL, Dremio, DuckDB, Fabric, MySQL, Oracle, PostgreSQL, Redshift, Spark Dataframe, Snowflake, SQL Server, Synapse, and Trino. The Free plan is free and includes pipeline testing, metrics observability, and alerting and ticketing integrations. Team is $750 a month; Enterprise has no listed price. Soda lists SOC 2 Type 2, SOC 3, DORA, and GDPR under compliance. It was founded in 2019 and lists locations in Chicago, Illinois, and Brussels, Belgium.