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Answer: Configuring each service to send logs to a centralized Azure Log Analytics workspace and using Kusto Query Language (KQL) for cross-service analysis
Configuring each service to send logs to a centralized Azure Log Analytics workspace and using Kusto Query Language (KQL) for cross-service analysis is the most suitable approach for ensuring comprehensive monitoring and logging across all the services in an end-to-end data pipeline. This method provides centralized visibility, powerful querying capabilities, real-time monitoring, scalability, and efficiency, making it the ideal choice for monitoring complex data pipelines in Azure.
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To achieve comprehensive monitoring and logging across an end-to-end data pipeline that spans multiple Azure services such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics, what is the most effective approach?
A
Developing custom logging within each component of the pipeline and storing logs in Azure Blob Storage for manual review
B
Using Azure Monitor‘s built-in integration with each service for individual monitoring, without central log aggregation
C
Configuring each service to send logs to a centralized Azure Log Analytics workspace and using Kusto Query Language (KQL) for cross-service analysis
D
Implementing Azure Event Grid to trigger alerts based on log events from each service, analyzed in Azure Functions for cross-service insights
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