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Quickstart - dbt users

This introduction assumes you are already using dbt in your company and tables you would like to monitor are managed by dbt. To fully use re_data you would need to install both:

We'll go over the steps required to do that & explain what possibilities those packages create for you.

Installing re_data dbt package

Add the re_data dbt package to your main dbt repo project. You need to update your packages.yml file with re_data package like that:



- package: re-data/re_data
version: [">=0.10.0", "<0.11.0"]

And then install dbt packages dependencies by running:

dbt deps

You can do that locally, in your dbt cloud environment, or Airflow etc. scheduler enviornment.


On production, you most likely are already running dbt deps as part of dbt models computation. So this step maybe only necessary for your local environment.

Configuring tables

Computing metrics & anomalies for your dbt models & sources requires configuring them to be observed by re_data. You can do it in a couple of ways, all of them described in re_data configuration reference part. A simple configuration for a single model contains just information that the model should be monitored & timestamp expression (usually column name) to be used when computing re_data time-based stats.

select ...

dbt package functionality

Let's go over some of the things you already can use with re_data dbt package.

For specifics look into reference section:

dbt auto generated documentation, together with our models graph is also available: here


re_data macros don't require any configuration and can be just used after you add re_data into your environment.

Computing first metrics

To compute re_data models containing metrics & anomalies you can just run standard dbt command.

dbt run --models package:re_data

single re_data run produces single data points about your tables for a time window. The default time window when you run re_data without parameters is yesterday. (from yesterday's 00:00 AM up until today 00:00 AM) To compare tables over time you would need to run the re_data dbt package multiple times (by some scheduler, re_data python package or manually).

The following would create tables inside your {default_schema}_re schema of your database. This is configured in dbt and can be overwritten in your dbt_project.yml.

Storing tests history (optional)

re_data enables you to store dbt tests results to investigate them later on. You can enable this functionality by setting:

re_data:save_test_history: true

In your dbt_project.yml file. After that when you run:

dbt test

re_data will store test history and with option --store-failures is added, it will also store failures in re_data_test_history model.

Installing re_data python package

To generate re_data reliability UI, send re_data alerts to Slack and easily backfill re_data models you will need to install the re_data python library. For this step, you need to have a python environment (with dbt installation) setup. Install re_data by executing:

pip install re_data

re_data python library should be installed in the same python environment where your dbt is installed. re_data makes use of dbt to run queries against your database. Because of that, you don't need to pass any DB credentials to re_data configuration. re_data by default will run dbt with the same credentials & profiles which you have in your dbt_project.yml and ~/.dbt/profiles.yml files. You can also change this behaviour by passing options to the re_data command.

Python package functionality

Python package add enabled you to use this functionality:

Generate & Serve UI

Let's go over 2 commands for generating & serving UI. It works quite similarly to dbt docs. First you create files by calling re_data overview generate and then serving already existing files by re_data overview serve. For more details on paramters accepted by this & other re_data commands check re_data CLI reference

re_data overview generate
re_data overview serve

Learning more

More detailed instrutions on running re_data are described in out toy_shop example tutorial 😊


Got stuck anywhere?

If you have more questions, got stuck anywhere, or something is not working as expected, please let us know on Slack! 😊, we will help you asap, and it will help us improve this quick start guide.