Amazon Deequ vs Great Expectations

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.

Great Expectations (GX) is a framework for defining data tests and checking whether data meets their criteria. GX Core is a Python library for building and running data-validation workflows, and it requires Python versions 3.10 through 3.13. GX Cloud provides a web interface for data teams, including technical and nontechnical stakeholders, and runs tests in the environment where connected data resides using read-only connections. Data Docs turns Expectations, validation results, and related metadata into static web pages. GX lists data-source integrations including Databricks, BigQuery, Pandas, PostgreSQL, Snowflake, Spark, Redshift, and cloud storage services. Notification integrations include Slack, Email, PagerDuty, OpsGenie, and Microsoft Teams; orchestration integrations include Apache Airflow, Dagster, and Prefect. Custom rules can be written with SQL in GX Cloud or through GX Core. The Developer plan costs nothing and covers up to five data assets under test per month and three users, with unlimited rows per asset. Team and Enterprise have custom data-asset limits and no listed prices.