Dynamic Task Mapping

Databricks Workflows offers a similar concept to Airflow's dynamic task mapping through the "For each" task type

1. Create a "For each" task in your Databricks Workflow.
2. Define the iterable items (similar to what you'd pass to .expand() in Airflow).
3. Specify a nested task that will be executed for each item in the iterable.

For example, if you have a list of dates to process, you could set up a "For each" task that iterates over these dates and runs a notebook or Python wheel for each one.

Reference: https://docs.databricks.com/en/jobs/for-each.html 

In Databricks Workflows, there isn't a direct equivalent to Airflow's get_current_context() function. However, you can access similar information through different means:

1. Job Parameters: You can define job-level parameters that are accessible to all tasks within the workflow.
   
   
2. Task Values: Databricks Workflows supports "Task Values," which allow you to set and retrieve small values from tasks. This can be used to pass information between tasks in a workflow.
   
   
3. Dynamic Values: Databricks Workflows supports dynamic value references, which allow you to access certain runtime information. For example:
   • {{job.run_id}} gives you the current job run ID
   • {{job.start_time}} provides the job start time
     
     
4. Notebook Parameters: If you're using notebook tasks, you can pass parameters to the notebook, which can include runtime information.
   
   Reference: https://docs.databricks.com/en/jobs/job-parameters.html and https://docs.databricks.com/en/jobs/task-parameters.html