Flowman supports many relational databases via JDBC connectivity.
New databases can be implemented on request without much effort.
Actually Flowman is neither a replacement for schedulers like Apache Airflow or Oozie, nor does it contain a job scheduler which automatically starts the execution of jobs at specific times. Since job scheduling is an overarching topic which is required to run many different tools, this is not a shortcoming of Flowman itself, but rather a design decision to exclude this feature since other excellent tools already exist.
This means you can use any existing scheduler which supports starting a bash script (since this is what the Flowman executables essentially are), so for example Oozie or Airflow work just fine.
Since Flowman version 0.30.0, AWS EMR is fully supported as a deployment target. You can either deploy Flowman to the master node and access it via ssh, or you can deploy Flowman as a step function of your EMR cluster. Flowman also supports EMR Serverless, but no interactive console access is possible.
Since Flowman version 1.0.0, Flowman supports Azure Synapse Spark as a deployment target. This means that you can create a fat jar containing all required Flowman libraries and your project, copy the jar file to Azure Blob Storage and then run Flowman as a Spark job inside Azure Synapse.
A gentle introduction into Flowman, the problem it solves and its core concepts.
How to set up Flowman locally to get started
A small quickstart guide will lead you through a simple example.
Streamline your development workflow by making most of all Flowman tools