![]() And because Airflow can connect to a variety of data sources – APIs, databases, data warehouses, and so on – it provides greater architectural flexibility. Prior to the emergence of Airflow, common workflow or job schedulers managed Hadoop jobs and generally required multiple configuration files and file system trees to create DAGs (examples include Azkaban and Apache Oozie ).īut in Airflow it could take just one Python file to create a DAG. There are also certain technical considerations even for ideal use cases.
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