Just make sure, that the used service role has the appropriate permissions to access those services. You could also query the Secret Manager, Parameter Store etc., if you prefer to save your credentials there. To do so, we query a AWS Glue Connection (you’ll need to create this before) via Python using the service role of the task that the container is run in (more on that later). Instead, what we do here is get the credentials in a secure way at runtime. I’m sure you can find many more anti patterns. For example, storing your credentials in the Dockerfile itself is a bad idea. I guess, there are numerous ways to achieve the first one. Run all dbt commands that are relevant for this project.Import the credentials to be used by dbt for connecting to our db on runtime.S3://dbt-documentation- $ENV/ $PROJECT_NAME/ dbtĮcho "" echo "Copying dbt outputs to s3://dbt-documentation- $ENV / $PROJECT_NAME / for hosting" aws s3 cp -recursive -exclude = "*" -include = "*.json" -include = "*.html" dbt/target/ \ dbtĮcho "" dbt source freshness -profiles-dir. redshift_credentialsĮcho "Running dbt:" echo "" dbt deps -profiles-dir. redshift_credentials file echo "Exporting Redshift credentials as environment variables to be used by dbt". scripts/export_glue_redshift_connection.py # writes. py script to get glue connection redshift credentials" python3. # We use this to securely import our Redshift credentials on runtime for the dbt profile echo "Running. Docker configurationįirst, let’s configure the Docker image that will later be deployed to AWS ECS and in which dbt will be executed. So, instead of showing you where to click in the AWS console, I’ll share the Terraform configuration. But worry not fellow Data Engineer, we’ll go through the central parts together.Įverything is implemented as IaC using Terraform. While this solution strives for maximum simplicity of usage, the implementation itself is a bit more tricky. Hard to beat this! Step by Step implementation Allows observability of run logs via CloudWatchįrom the viewpoint of an Analyst deploying a model into production (obviously first into dev) is as simple as doing git push.Exports dbt run results to S3 bucket for auto generating documentation.Runs Docker Image on AWS ECS using serverless Fargate instances.Uses a Docker Image as dbt run environment.The simplified architecture diagram for our solution looks like this: You might want to give it a try if you are on AWS and are looking for a reliable and maintainable way to let users deploy their dbt models into production and run them on a schedule. In this post, I present a tested step by step solution, that has worked great in my experience. However, if you can’t do that, there are some good alternatives out there. Your best bet for that is probably using dbt cloud. A smooth deployment to production is central for this. often times Analysts) in your org as easy as possible when introducing dbt. It’s also crucial to make the life of users (i.e. Hence, for increasing adaptation of dbt, it’s not enough to point out its benefits over the “traditional” way of doing transformations. And it seems that some Analysts really love stored procedures and spaghetti code. Usually, people don’t like to change the way they work.
#DBT AIRFLOW CODE#
The era of data transformations based upon non-versioned stored procedures and unreadable Python code is finally over… Not so fast! Dbt has established itself as a great tool for empowering analysts to take ownership of the analytical layer in the data warehouse.
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