Amazon Deequ vs Datafold
Amazon Deequ
github.com
Datafold
datafold.comSide by side
| Figure | Amazon Deequ | Datafold |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Database Testing Tools | 8.7 (#3) | 8.1 (#10) |
| Integrations | Deequ is built on Apache Spark, is distributed through Maven artifacts, and has a Python interface called PyDeequ. | The product page names Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, GitHub, and GitLab, and says Datafold integrates with 50+ data tools. |
| Purpose | Deequ is an Apache Spark library for defining unit tests that measure data quality in large datasets. | Datafold combines specialized AI agents, a context layer, and data quality tools to automate data engineering. |
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.