Databricks Certified Data Engineer - Professional
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To enhance logging for a complex data pipeline in Databricks, ensuring comprehensive tracking of data flow, transformations, and bottleneck identification, which approach would you choose?
To enhance logging for a complex data pipeline in Databricks, ensuring comprehensive tracking of data flow, transformations, and bottleneck identification, which approach would you choose?
Real Exam
Explanation:
- Utilizing Databricks native logging features: Databricks offers built-in logging capabilities that track your data pipeline's execution, including details on data flow, transformations, and job status. This eliminates the need for extensive custom logging. 2. Integrating with Azure Monitor: Azure Monitor provides a centralized solution for log management and analysis. By integrating Databricks logs with Azure Monitor, you can centralize logs for easier analysis and troubleshooting, enhancing monitoring and alerting. 3. Comprehensive and actionable logging: Combining Databricks native logging with Azure Monitor ensures all necessary information is logged for tracking data flow, identifying bottlenecks, and monitoring jobs, offering insights to optimize pipeline performance. This approach is ideal for comprehensive and actionable logging in complex Databricks data pipelines.