Hello, @Siemens Healthineers Madivalappa_Azure Account
Let’s dive into the differences between the Apache Airflow providers packages and the pip-installable packages and how they relate to Managed Airflow in Azure Data Factory (ADF).
Apache Airflow Providers Packages:
What Are They?: Apache Airflow is modular, and its core functionality (scheduler, basic tasks) is delivered as the apache-airflow package. However, additional capabilities can be added by installing separate packages called providers.
What Do Providers Contain?: Providers include operators, hooks, sensors, and transfer operators that interface with various external systems. They can also extend Airflow core with new features.
Community-Managed Providers: The Apache Airflow community maintains over 80 provider packages. These packages are versioned separately from the core Airflow releases.
Custom Providers: You can even create your own custom providers with the same capabilities as community-provided ones.
Example: For specific services like Amazon or Google, you’ll find provider packages like apache-airflow-providers-amazon or apache-airflow-providers-google.
Pip-Installable Packages:
These are Python packages that can be installed using pip. They include not only the core Airflow (apache-airflow) but also any additional dependencies.
Decoupling: Starting from version 1.8, installing an adapter (like dbt-snowflake) no longer automatically installs dbt-core. Adapters and dbt Core versions are now decoupled to avoid overwriting existing installations.
Installing dbt-Snowflake: You can install the dbt-snowflake adapter using pip install dbt-snowflake.
Managed Airflow in Azure Data Factory (ADF):
What Is It?: ADF offers a managed orchestration service for Apache Airflow called Workflow Orchestration Manager.
Integration: It allows you to run Apache Airflow DAGs (Directed Acyclic Graphs) within ADF, providing extensibility for orchestrating Python-based workflows at scale on Azure.
Benefits:
Azure Reliability: ADF combines Azure’s reliability, scale, security, and ease of management with Airflow’s extensibility.
Multi-Orchestration: ADF now supports both visual, UI-based pipelines and code-centric, Python-based DAGs (like those in Airflow).
Use Cases:
If you’re familiar with Apache Airflow or currently use it, you might prefer Managed Airflow within ADF.
If you prefer not to write/manage Python-based DAGs, stick with ADF pipelines.
I hope this info is helpful to you.
Best Regard,
Annie Johnston
DollarTreeCompass