![]() ![]() Manifest = json.load(f) # Load its contents into a Python Dictionary With open(manifest_path) as f: # Open manifest.json Manifest_path = os.path.join(dbt_path, "target/manifest.json") # path to manifest.json ![]() HOME = os.environ # retrieve the location of your home folderĭbt_path = os.path.join(HOME, "repos/demo/dbt/my_project") # path to your dbt project This is the part we can use to plug dbt into Airflow.įrom import BashOperator This helps you get the full testing experience in a demo model.ĭbt also has a great framework for automating model documentation. The example purposely includes some passing tests and some that fail. Ġ0:56:12 Finished running 4 tests in 0.15s.Ġ0:56:12 Completed with 1 error and 0 warnings:Ġ0:56:12 Failure in test not_null_my_first_dbt_model_id (models/example/schema.yml)Ġ0:56:12 Got 1 result, configured to fail if != 0Ġ0:56:12 compiled SQL at target/compiled/my_project/models/example/schema.yml/not_null_my_first_dbt_model_id.sqlĠ0:56:12 Done. Ġ0:56:12 4 of 4 PASS unique_my_second_dbt_model_id. Ġ0:56:12 4 of 4 START test unique_my_second_dbt_model_id. Ġ0:56:12 3 of 4 PASS unique_my_first_dbt_model_id. Ġ0:56:12 3 of 4 START test unique_my_first_dbt_model_id. Ġ0:56:12 2 of 4 START test not_null_my_second_dbt_model_id. Switch over to the postgres terminal so we can run some SQL commands.įirst, let’s check if my_first_dbt_model.sql produced a cd ~/repos/demo/dbt/my_project & dbt testĠ0:56:12 Found 2 models, 4 tests, 0 snapshots, 0 analyses, 167 macros, 0 operations, 0 seed files, 0 sources, 0 exposures, 0 metricsĠ0:56:12 Concurrency: 1 threads (target='dev')Ġ0:56:12 1 of 4 START test not_null_my_first_dbt_model_id. Let’s verify that these models actually made tables in our database. Ġ0:52:06 2 of 2 OK created view model dev.my_second_dbt_model. Ġ0:52:06 2 of 2 START view model dev.my_second_dbt_model. Ġ0:52:06 1 of 2 OK created table model dev.my_first_dbt_model. To install brew, cd ~/repos/demo/dbt/my_project & dbt runĠ0:52:06 Found 2 models, 4 tests, 0 snapshots, 0 analyses, 167 macros, 0 operations, 0 seed files, 0 sources, 0 exposures, 0 metricsĠ0:52:06 Concurrency: 1 threads (target='dev')Ġ0:52:06 1 of 2 START table model dev.my_first_dbt_model. The preferred installation method for pyenv and pyenv-virtualenv is brew. However, Airflow v2.3.2 and dbt v1.1.0 have conflicting libraries, so you have to follow best practices for this demo. Note: Sometimes you can get away with not using virtual environments. For this demo, we will use Pyenv to install Python, and Pyenv Virtualenv (an extension of Pyenv) for managing Python Virtual Environments. You need extra software to manage the shell environment for package dependencies. ![]() Mobile apps are compiled into a single binary file, whereas Python packages are a collection of text files. The app contains everything it needs, even if this means every app on the device has a copy of the same library.ĭbt and Airflow are Python Packages, and Python Packages do not work like mobile apps. For example, even if the Lyft and Uber apps both use a few of the same libraries, they don’t share those libraries from some shared location. If you’ve used an iOS or Android device, you might intuit that mobile apps are self-contained. ![]() For this reason, dbt and Airflow will be installed directly on your machine. SSH communication across containers is a complex topic and requires extra configuration in both Airflow and Docker. If we ran these apps in containers, the only way Airflow could run dbt-CLI commands would be to connect the Airflow container to the dbt container over SSH. The open-source version of dbt is a command-line tool. We'll use it later.įor this demo, we will not be running dbt or Airflow in containers like we did with Postgres. $ cd ~/repos/demo/postgres & docker-compose down ![]()
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