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Deployment 🚀

Running re_data on production

re_data is designed to fit into existing workflows and can be run in many different ways. We will describe here the most common ways of running re_data in production.

In most cases, you would like to run re_data in the same place where your dbt models are run. That's why we will suggest you couple of options depending on how you are running dbt yourself.

run dbt on Airflow, Prefect, or other orchestration tools

If you are using any of the popular orchestration tools, we advise you to create a new job that will run re_data after your dbt runs. This way you can be sure that re_data will be run after your dbt models are refreshed.

All of the orchestration tools allow quite easily run bash commands. The most important thing here is to make sure re-data is actually available in the orchestration tool's environment. You can easily install re_data even in the same command running it by using pip install re-data.

run dbt in dbt cloud

If you are running dbt in the dbt cloud, there are two options you can use to run re_data. Firstly re-data is a dbt package and as such can be run in the dbt cloud by running a command

 dbt run --select package:re_data

This can compute all the backend models of re_data in your dbt cloud environment.

Unfortunately, it's not possible to run the re_data run command directly in the dbt cloud, for this reason, if you want to generate re-data UI we are recommending you set up a Github Action which will run re_data overview command. This is the way we are running re_data (and dbt) ourselves, and we are happy to share our setup with you.

re_data (and possibly dbt) run in GitHub Actions

As mentioned this is how we are running dbt and re_data, and re_cloud ourselves so that it can be considered our favorite :) We recently make our analysis repo public so you can see easily see and copy all the setup required for running dbt and re_data this way. You can check out the repo: HERE. The most interesting part of it is the GitHub Actions which is running our whole setup, you can check our github action file here

One thing to notice is how to easily point re_data to the configuration file without making them part of the repo. re_data support variable RE_DATA_CONFIG_DIR which can be used to point to a directory where your configuration files are stored. This way is very similar to dbt's DBT_PROFILES_DIR env variable, which we are also using.

Lastly, we are using yq to fill the proper values in our configuration files (from GitHub secrets) at the time of running the commands.

More questions?

Having more questions about this, join our Slack! 😊 and let us know!