![]() Right, back on topic! The Airflow-dbt-Python provider, shared by Dani, has instructions for installation! Even with a section for MWAA! As suspected, you need to change the requirements.txt file to include the name of the provider.įrom Astronomer registry , it looks as though the name of the Fivetran provider should be Airflow-provider-Fivetran and the dbt cloud one should be Apache-Airflow-providers-dbt-cloud. This is a whole ecosystem in its infancy - look ahead at the Software Engineering ecosystem. It’s all of the above, working together, chipping away at the same problems until they’re solved. Not just practitioners hacking it together on their own I am intending to use dbt cloud, but if I were going to use dbt core, this would have been incredibly helpful - especially as it’s been well-tested with MWAA!Īs a tangent, this is why I believe that we will solve the problems in the Modern Data Stack, as a community: From seeing that there is a requirements.txt file, I’m guessing this is how, but it’s not that obvious to someone new to this kind of tool! I was hoping to find the equivalent of this page in the dbt docs.ĭani Sola - SVP of Data at Clark - kindly shared a link to an Airflow Provider for dbt that one of his team, plus some additional contributors, have made. The links above mention providers that could be really helpful for me, as they will give me pre-built operators (plus hooks and sensors which I haven’t learned about yet) for Fivetran and dbt cloud.īut how do you install a provider in Airflow? Strange that these docs don’t actually tell you how, but just describe what is possible. Now that I’ve completed the parts of the course around operators and dependencies, it’s probably a good time to try and build some in Metaplane’s Airflow. This workflow would be very cumbersome for large DAGs that are maintained across bigger teams. I can see why companies using Airflow instead of dbt in the past usually didn’t have DAGs as large or as complex as those using dbt. I wrote an article about macros, variables and templating that I do recommend you to read - Introduction to Airflow in Python Create a Python file in your folder dags/ and paste the code below: from airflow import DAG Let’s say you want to get the price of specific stock market symbols such as APPL (Apple), FB (Meta), and GOOGL (Google). That means the DAG must appear in globals(). You must know that Airflow loads any DAG object it can import from a DAG file. ![]() Ok, now let me show you the easiest way to generate your DAGs dynamically. Notice that an AIP Dynamic Task Mapping is coming soon. Apache Airflow needs to know what your DAG (and so the tasks) will look like to render it. ![]() Today, it’s not possible (yet) to do that. The latter is when you make tasks based on the output of previous tasks. The former is when you create DAGs based on static, predefined, already known values (configuration files, environments, etc.). Thanks to that, it’s pretty easy to generate DAGs dynamically.īefore I show you how to do it, it’s important to clarify one thing.ĭynamic DAGs are NOT dynamic tasks. The beauty of Airflow is that everything is in Python, which brings the powerfulness and flexibility of this language. □ The confusion with Airflow Dynamic DAGs Guess what? That’s what dynamic DAGs solve. if you move from a legacy system to Apache Airflow, porting your DAGs may be a nightmare without dynamic DAGs.it’s harder to maintain as each time something change, you will need to update all of your DAGs one by one.you waste your time (and your time is precious).The bottom line is that you don’t want to create the same DAG, the same tasks repeatedly with just slight modifications. destination table (could be a different table for each API route, folder etc)Īlso, you could have different settings for each of your environments: dev, staging, and prod.staticstics (could be mean, median, standard deviation, all of them or only one of those).source (could be a different FTP server, API route etc.).Now, let’s say this DAG has different configuration settings.
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