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Cross-Project Dependencies Handling with DBT in AWS MWAA
- Cross-project dependencies handling can be tricky in data engineering, and DBT helps define models in SQL that are organized into projects. The complexity of data pipelines often means different DBT projects have dependencies on models in other projects, so managing such dependencies is critical for smooth data pipeline execution. AWS Managed Workflows for Apache Airflow (MWAA) helps orchestrate DBT projects in the cloud and provides scalability and reliability for large data pipelines. It's not always easy to manage SSH keys dynamically during DAG execution in MWAA environments, and executing tasks in isolated Kubernetes pods may simplify dependency handling and CI/CD processes.
- Using PythonVirtualenvOperator in Airflow helps create isolated virtual environments to run Python code, including DBT commands, allowing operators to install additional libraries and dependencies specific to each task. To handle secure access to a private dbt project from a Git repository in a DAG in Airflow, the SSH private key is stored in AWS Secrets Manager and used to run the dbt deps command inside the virtual environment during DAG execution. Packages.yml file needs to be updated to include cross-project dependencies locally using the local keyword.
- SSH key issues may arise when executing DBT projects in MWAA, and the PythonVirtualenvOperator approach can add complexity to CI/CD pipelines, while the custom KubernetesPodOperator can simplify handling dependencies.
- AWS Managed Workflows for Apache Airflow (MWAA) enables the deployment of production-grade workflows, including ETL, machine learning, and scientific computing pipelines. Terraform can be used to create the MWAA environment, manage Airflow DAGs, and configure other essential resources, while the custom plugins feature in Apache Airflow allows users to extend Airflow’s core functionality by adding custom operators, sensors, hooks, and other components.
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