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Answer: Create a multi-task job where each task corresponds to a notebook, configure task dependencies to enforce the sequence, and use shared volumes to pass data between tasks.
Creating a multi-task job in Databricks allows for the automation of complex workflows where each task (notebook) depends on the output of another. This approach ensures that the sequence is maintained and data dependencies are handled efficiently.
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You are developing a Databricks job that requires the execution of multiple notebooks in a specific sequence. Each notebook processes different parts of a large dataset. How would you ensure that the notebooks are executed in the correct order and that each notebook has access to the outputs of the previous notebooks?
A
Use the Databricks CLI to manually execute each notebook in sequence and manually pass outputs between notebooks.
B
Create a multi-task job where each task corresponds to a notebook, configure task dependencies to enforce the sequence, and use shared volumes to pass data between tasks.
C
Modify each notebook to include code that checks for the existence of previous notebook outputs before proceeding.
D
Schedule each notebook to run at a specific time, ensuring that the timing aligns with the completion of the previous notebook.
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