Explanation:
Option B is the most suitable approach for dynamically adjusting Databricks job schedules based on external triggers using the Databricks CLI. Here‘s why:
Automation: The Databricks CLI with a JSON payload allows for automation, enabling external triggers to automatically generate the necessary JSON payload to update job schedules without manual intervention.
Flexibility: Dynamically generating the JSON payload offers the flexibility to adjust job schedules based on various external triggers or events, creating a more responsive and adaptable scheduling system.
Efficiency: Manual updates via the Databricks UI (Option C) can be time-consuming and error-prone. Using the CLI with a JSON payload streamlines the process and reduces human error.
Integration: Integrating the Databricks CLI with an external application to generate the JSON payload ensures seamless communication between systems, allowing job schedules to be adjusted in real-time based on external events.
Scalability: While implementing a continuous integration pipeline (Option A) may be complex for adjusting job schedules, using the Databricks CLI with a JSON payload provides a straightforward and scalable solution for dynamic scheduling.
In conclusion, Option B efficiently automates the process of dynamically adjusting Databricks job schedules based on external triggers using the Databricks CLI.
Ultimate access to all questions.
How can you dynamically adjust Databricks job schedules based on external triggers using the Databricks CLI?
A
Implement a continuous integration pipeline that triggers databricks jobs reset commands based on external events.
B
Use databricks jobs update with a JSON payload dynamically generated by an external application to adjust schedules.
C
Manually update job schedules via the Databricks UI in response to external triggers.
D
Rely solely on the REST API for dynamic scheduling, as the CLI does not support job schedule modifications.
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