DataKitchen TestGen vs Great Expectations
DataKitchen TestGen
datakitchen.ioGreat Expectations
greatexpectations.ioSide by side
| Figure | DataKitchen TestGen | Great Expectations |
|---|---|---|
| Price from | $100/mo | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 3 |
| Spec score · Database Testing Tools | 9.3 (#2) | 8.2 (#9) |
| Founded | 2013 | 2018 |
| Headquarters | Lexington, Massachusetts, United States | Cottonwood Heights, Utah, United States |
| Notifications | Issue reports and alerts can be sent by email, Slack or Microsoft Teams. | GX lists Slack, Email, PagerDuty, OpsGenie, and Microsoft Teams as notification-action integrations. |
| Security | The security documentation describes HTTPS support, AES-256-CBC encryption for database credentials at rest, and enterprise SSO through OpenID Connect. | GX Cloud metadata is encrypted at rest with AES-256 and in transit with TLS 1.2, and tenant isolation uses Postgres Row Level Security. |
Plans
Great Expectations
- DeveloperFree
- EnterprisePriced on request
- TeamPriced on request
In their own words
DataKitchen TestGen
DataKitchen TestGen profiles databases, generates data quality tests, and shows results and quality scores in dashboards. Its listed checks include 55 profiling characteristics, 32 hygiene detector tests, and 49 test types, including generated tests, business rules, and custom SQL. Monitors cover freshness, volume, schema, and custom metrics, using predictive models to flag changes such as late arrivals and schema drift. Supported databases include Snowflake, Databricks SQL, Azure Synapse Analytics, Azure SQL Database, SQL Server, OneLake, BigQuery, Redshift, Aurora PostgreSQL, Oracle 12c and later, SAP HANA, PostgreSQL, and Salesforce Data 360. It can also profile and test structured data in Iceberg, Parquet, Avro, ORC, CSV, and JSON through supported external-table options. TestGen runs in the customer’s infrastructure; DataKitchen says it does not host the application or receive customer data. It queries target databases with read-only access and stores results and metadata in its application database. The free Open Source plan is limited to one user, one connection, and one project. Enterprise costs $100 a month, billed per user and per database connection.
Great Expectations
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.