The post explores various methods to run the dlt Python library with Apache Airflow, including PythonOperator, PythonVirtualenvOperator, KubernetesPodOperator, and external services. It details the advantages and challenges of each method, emphasizing the importance of addressing module conflicts, resource contention, and separation of scheduling and execution code. KubernetesPodOperator is highlighted for its efficient decoupling of scheduling from execution, suitable for those with the necessary skills in Kubernetes and CI/CD.
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Using the PythonOperator Link iconUsing the PythonVirtualenvOperator Link iconExternal Services Link iconConclusion Link iconSort: