DataOps Data Quality TestGen, or "TestGen" for short, can help you find data issues so you can alert your users and notify your suppliers. It does this by delivering simple, fast data quality test generation and execution by data profiling, new dataset screening and hygiene review, algorithmic generation of data quality validation tests, ongoing production testing of new data refreshes, and continuous anomaly monitoring of datasets. TestGen is part of DataKitchen's Open Source Data Observability.
What does DataKitchen's DataOps Data Quality TestGen do? It helps you understand and find data issues in new data.
It constantly watches your data for data quality anomalies and lets you drill into problems.A single place to manage Data Quality across data sets, locations, and teams.
The dk-installer program installs DataOps Data Quality TestGen.
Software | Tested Versions | Command to check version |
---|---|---|
Python - Most Linux and macOS systems have Python pre-installed. - On Windows machines, you will need to download and install it. Why Python? To run the installer. |
3.9, 3.10, 3.11, 3.12 | python3 --version |
Docker Docker Compose Why Docker? Docker lets you try TestGen without affecting your local software environment. All the dependencies TestGen needs are isolated in its own container, so installation is easy and insulated. |
25.0.3, 26.1.1, 2.24.6, 2.27.0, 2.28.1 |
docker -v docker compose version |
On Unix-based operating systems, use the following command to download it to the current directory. We recommend creating a new, empty directory.
curl -o dk-installer.py 'https://github.com/raw/DataKitchen/data-observability-installer/main/dk-installer.py'
- Alternatively, you can manually download the
dk-installer.py
file from the data-observability-installer repository. - All commands listed below should be run from the folder containing this file.
- For usage help and command options, run
python3 dk-installer.py --help
orpython3 dk-installer.py <command> --help
.
The installation downloads the latest Docker images for TestGen and deploys a new Docker Compose application. The process may take 5~10 minutes depending on your machine and network connection.
python3 dk-installer.py tg install
The --port
option may be used to set a custom localhost port for the application (default: 8501).
To enable SSL for HTTPS support, use the --ssl-cert-file
and --ssl-key-file
options to specify local file paths to your SSL certificate and key files.
Once the installation completes, verify that you can login to the UI with the URL and credentials provided in the output.
The Data Observability quickstart walks you through DataOps Data Quality TestGen capabilities to demonstrate how it covers critical use cases for data and analytic teams.
python3 dk-installer.py tg run-demo
In the TestGen UI, you will see that new data profiling and test results have been generated.
The dk-installer and docker compose CLI can be used to operate the installed TestGen application. All commands must be run in the same folder that contains the dk-installer.py
and docker-compose.yml
files used by the installation.
After completing the quickstart, you can remove the demo data from the application with the following command.
python3 dk-installer.py tg delete-demo
New releases of TestGen are announced on the #releases
channel on Data Observability Slack, and release notes can be found on the DataKitchen documentation portal. Use the following command to upgrade to the latest released version.
python3 dk-installer.py tg upgrade
The following command uninstalls the Docker Compose application and removes all data, containers, and images related to TestGen from your machine.
python3 dk-installer.py tg delete
The testgen command line can be accessed within the running container.
docker compose exec engine bash
Use exit
to return to the regular terminal.
docker compose down
docker compose up -d
We recommend you start by going through the Data Observability Overview Demo.
For support requests, join the Data Observability Slack 👋 and post on the #support
channel.
Follow these instructions to improve the quality of data in your database.
Talk and learn with other data practitioners who are building with DataKitchen. Share knowledge, get help, and contribute to our open-source project.
Join our community here:
-
👋 Join us on Slack, this is also how you get support (see above)
For details on contributing or running the project for development, check out our contributing guide.
DataKitchen's DataOps Data Quality TestGen is Apache 2.0 licensed.