![]() DWH credentials) into your container, right? For this hands on purpose only we will try to keep it as simple as possible and pack our BigQuery KeyFile with our DBT project. Now you may be wondering why on earth would you pack your profile (a.k.a. In order to achieve that, we need to build a Dockerfile that will pack everything that is needed to run DBT, from a proper Python environment to the profile file used on the project. The strategy behind this approach is really simple: we want to have a Docker Image that will allow us to launch an instance of our DBT project environment and execute a DBT command inside it. To achieve that you can follow this step-by-step guide! One last requisite is to have Docker Desktop installed on your system because we will need to deploy and start containers to run our DBT tasks. By following this tutorial you should be ready to use Airflow for this exercise. This whole hands on has been tested on a Windows 10 machine while using native Ubuntu virtualization from Windows 10 app store to run Airflow.Īlso, make sure to set your Airflow instance running with the Local Executor, or equivalent, in order to have parallelism enabled. At the end of this post, you should be able to automate your DBT project while merging its awesome features with Airflow useful Gantt charts and debug.įirst of all, in order to keep it as simple as possible for new DBT users, we will use this Fishtown Analytics tutorial as our starting point, so you should have your Jaffle Shop project sitting somewhere in your local file system before proceeding. The orchestration will be handled by Airflow (and if you don’t know it yet, make sure to check this post first) through the use of Docker containers to run the whole DBT model and test it. ![]() What am I missing or how can I leverage a requirements.So, now that we’ve discussed what is DBT and why you should it along with your ELT stack, it’s finally the time to talk about how to actually implement it in a real life situation using an orchestration tool.Įven though you could always use the simplified implementation of DBT based on the SaaS solution of DBT Cloud, sometimes we would like to keep everything being executed from within our own place ( our data our rules), right? In order to fill the gap of manual implementation of DBT as a command line tool software, we will automate the execution of each project task through an orchestrator. In other words, I am doing something wrong and the PythonVirtualenvOperator is not properly handling my requirements.txt file. This seems to indicate that PythonVirtualenvOperator is treating my requirements param like a list instead of a string. Neither 'setup.py' nor 'pyproject.toml' found. t x tĮRROR: Directory '/' is not installable. However, when the task runs, I quickly receive an error: Executing cmd: /tmp/venvfn63d圓c/bin/pip install m o d u l e s / m o n d a y / r e q u i r e m e n t s. This seems to fit the implementation described in GitHub, because requirements is a string, not a list, and it complies with the *.txt template. Sync_board_items(board_id=XXXX, table=XXXX) The task I'd like to reference requirements.txt for is defined like so: sync_board_items():įrom import sync_board_items I am seeking guidance on how to properly utilize a requirements.txt file with Airflow's PythonVirtualenvOperator. However, I am unable to properly utilize that parameter. In Airflow 2.2.3, PythonVirtualenvOperator was updated to allow templated requirements.txt files in the requirements parameter.
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