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Answer: Deploy the batch jobs using Azure Databricks, implementing comprehensive monitoring with Azure Monitor to track performance metrics, system health, and error logs, and setting up alert rules for proactive issue detection.
The best strategy is to deploy the batch jobs using Azure Databricks and implement comprehensive monitoring with Azure Monitor. This approach meets the project's requirements by ensuring cost efficiency through the use of a managed service, compliance with data governance policies, scalability to handle varying workloads, and high availability. Comprehensive monitoring, including performance metrics, system health, and error logs, along with alerting mechanisms, enables early detection of issues, minimizing downtime. The other options either lack the necessary monitoring capabilities, do not fully leverage Azure's managed services for cost efficiency and scalability, or ignore critical monitoring aspects, making them less suitable for the given scenario.
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You are a Data Engineer working on a project that requires the deployment of batch jobs in a production environment on Microsoft Azure. The project has strict requirements for cost efficiency, compliance with data governance policies, and the ability to scale based on workload demands. Additionally, the team needs to ensure high availability and early detection of any issues to minimize downtime. Considering these constraints, which of the following strategies would you implement to deploy and monitor the batch jobs effectively? (Choose one option)
A
Deploy the batch jobs using Azure Virtual Machines without any monitoring tools, relying solely on manual checks for issue detection, to minimize costs.
B
Deploy the batch jobs with Azure Batch service, enabling basic logging for error tracking but skipping advanced monitoring and alerting to reduce complexity.
C
Deploy the batch jobs using Azure Databricks, implementing comprehensive monitoring with Azure Monitor to track performance metrics, system health, and error logs, and setting up alert rules for proactive issue detection.
D
Deploy the batch jobs with Azure Functions, monitoring only the execution time of each job to keep the monitoring overhead low.