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Answer: Configure each service to send logs to Azure Log Analytics and use Kusto Query Language (KQL) for cross-service log analysis.
Configuring each service to send logs to Azure Log Analytics allows for centralized log aggregation, making it easier to monitor and analyze logs across multiple services. Using Kusto Query Language (KQL) provides a powerful querying language specifically designed for log analysis, enabling efficient cross-service log analysis. This approach also allows for real-time monitoring and alerting based on log data, providing insights into the health and performance of the entire data pipeline. While other options like using Azure Monitor or developing a custom solution have their merits, they may not offer the same level of efficiency and flexibility as Azure Log Analytics with KQL. Relying on native logging capabilities and manual correlation is inefficient and error-prone in complex scenarios.
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In a complex data pipeline that spans multiple Azure services, including Event Hubs, Databricks, and Synapse Analytics, what is the most effective method to aggregate logs across these services for centralized monitoring and analysis?
A
Develop a custom logging solution that aggregates logs in Azure Blob Storage for batch analysis in Databricks.
B
Configure each service to send logs to Azure Log Analytics and use Kusto Query Language (KQL) for cross-service log analysis.
C
Rely on the native logging capabilities of each service and manually correlate logs for troubleshooting.
D
Use Azure Monitor to collect logs from each service and analyze them using the built-in features of Azure Portal.
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