Refer to the exhibit. The JSON represents a Databricks Job configuration. To implement this as part of a CI/CD pipeline, which approach ensures the job is created with the exact specifications provided?
Exhibit
{
"name": "prod-job",
"tasks": [
{
"task_key": "ingest",
"notebook_task": {
"notebook_path": "/Shared/ingest"
}
}
],
"schedule": {
"quartz_cron_expression": "0 0 12 * * ?"
}
}Trap 1: Manually import the JSON into the Databricks Job UI for every…
Manual imports are prone to human error and do not offer a programmatic way to verify that the configuration matches the expected state. This approach lacks repeatability and makes it impossible to implement automated testing for the deployment process, leading to potential inconsistencies across different environments.
Trap 2: Upload the JSON to a DBFS location and trigger it using a Python…
Storing job definitions in DBFS does not facilitate job creation or management. DBFS is intended for data storage, not for managing workspace objects or job orchestrations. Relying on scripts to manually parse and create jobs introduces unnecessary complexity and potential bugs into the deployment automation process.
Trap 3: Hardcode the job configuration inside a notebook and run it via the…
Hardcoding infrastructure configuration inside a notebook logic tightly couples code with the execution environment. This makes it difficult to manage environment-specific variables, such as clusters or schedules, and complicates the testing process. Configuration should always be externalized and managed through proper infrastructure-as-code tools like DABs.
- A
Manually import the JSON into the Databricks Job UI for every release.
Why it fails: Manual imports are prone to human error and do not offer a programmatic way to verify that the configuration matches the expected state. This approach lacks repeatability and makes it impossible to implement automated testing for the deployment process, leading to potential inconsistencies across different environments.
- B
Use the Databricks CLI 'databricks jobs create --json-file job.json' command.
The CLI provides a robust, scriptable interface for managing jobs. By using the 'create' or 'reset' commands with a JSON file, the CI/CD pipeline can ensure that the production job perfectly mirrors the definition stored in the code repository, eliminating manual intervention and ensuring consistent deployments across environments.
- C
Upload the JSON to a DBFS location and trigger it using a Python script.
Why it fails: Storing job definitions in DBFS does not facilitate job creation or management. DBFS is intended for data storage, not for managing workspace objects or job orchestrations. Relying on scripts to manually parse and create jobs introduces unnecessary complexity and potential bugs into the deployment automation process.
- D
Hardcode the job configuration inside a notebook and run it via the job.
Why it fails: Hardcoding infrastructure configuration inside a notebook logic tightly couples code with the execution environment. This makes it difficult to manage environment-specific variables, such as clusters or schedules, and complicates the testing process. Configuration should always be externalized and managed through proper infrastructure-as-code tools like DABs.