DBT Cloud (via Collector method) - v3.2.0

About Collectors

Pre-requisites

• Python 3.8 - 3.11

• Access to K landing directory

• Access to DBT Cloud

Step 1: Create the Source in K

Create an DBT Cloud source in K

• Go to Settings, Select Sources and click Add Source

• Select “Load from File system” option
  
  

  Open

  

   

   

• Give the source a Name - e.g. DBT Cloud Production

• Add the Host name for the DBT Cloud Server

• Click Finish Setup

Step 2: Getting Access to the Source Landing Directory

Step 3: Install the Collector

It is recommended to use a python environment such as pyenv or pipenv if you are not intending to install this package at the system level.

Some python packages also have dependencies on the OS level packages, so you may be required to install additional OS packages if the below fails to install.

You can download the Latest Core Library and whl via Platform Settings → Sources → Download Collectors

Open

Run the following command to install the collector

You will also need to install the common library kada_collectors_lib for this collector to function properly.

Step 4: Configure the Collector

The collector requires a set of parameters to connect to and extract metadata from DBT Cloud

These parameters can be added directly into the run or you can use pass the parameters in via a JSON file. The following is an example you can use that is included in the example run code below.

kada_dbt_extractor_config.json

Step 5: Run the Collector

The following code is an example of how to run the extractor. You may need to uplift this code to meet any code standards at your organisation.

This can be executed in any python environment where the whl has been installed. It will produce and read a high water mark file from the same directory as the execution called dbt_hwm.txt and produce files according to the configuration JSON.

This is the wrapper script: kada_dbt_extractor.py

Advance options:

If you wish to maintain your own high water mark files elsewhere you can use the above section’s script as a guide on how to call the extractor. The configuration file is simply the keyword arguments in JSON format. Refer to this document for more information https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#Storing-HWM-in-another-location

If you are handling external arguments of the runner yourself, you’ll need to consider additional items for the run method. Refer to this document for more information https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#The-run-method

token: DBT Cloud Read Only API Token.
url: DBT cloud URL
account_id: account ID DBT Cloud, should be a numeric ID.
environment_ids: environment ID DBT Cloud, should be a numeric ID.
mapping: Dict of project ids to corresponding database hosts
timeout: Timeout for the API call
dry_run: Run the extractor for the purpose of producing
output_path: full or relative path to where the outputs should go
compress: To gzip output files or not

Step 6: Check the Collector Outputs

K Extracts

A set of files (eg metadata, databaselog, linkages, events etc) will be generated. These files will appear in the output_path directory you set in the configuration details

High Water Mark File

A high water mark file is created in the same directory as the execution called dbt_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.

If you want prefer file managed hwm, you can edit the location of the hwn by following these instructions https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#Storing-High-Water-Marks-(HWM)

Step 7: Push the Extracts to K

Once the files have been validated, you can push the files to the K landing directory.

You can use Azure Storage Explorer if you want to initially do this manually. You can push the files using python as well (see Airflow example below)

Example: Using Airflow to orchestrate the Extract and Push to K