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How to Monitor and Debug Pipelines in Azure Data Factory? AZURE DATA ENGINEERING
Title Key Insights for Ensuring Reliable Data Workflows • Monitoring and Debugging Pipelines in Azure Data Factory Subtitle
Introduction • Overview: Importance of monitoring and debugging pipelines. • Objective: Ensure data accuracy, performance, and reliability in Azure Data Factory workflows. • Scope: Discuss tools, techniques, and best practices for pipeline monitoring and debugging.
Azure Data Factory Monitoring Tools • Azure Monitor: Centralized monitoring solution. • Activity Runs: Tracks the status of individual pipeline activities. • Pipeline Runs: Provides end-to-end visibility of executions. • Alerts & Metrics: Set up alerts for failures, delays, and performance issues.
Debugging in Azure Data Factory • Debug Mode: • Test pipelines without triggering full executions. • Validate data transformations interactively. • Activity-Specific Testing: Isolate and test individual pipeline components. • Error Messages: Use detailed error logs for troubleshooting.
Pipeline Run Monitoring • Pipeline Runs View: • Visualize all executions with status (Success/Failed/In Progress). • View runtime and performance statistics. • Input/Output Data Validation: • Inspect data at each stage to identify discrepancies
Setting Up Alerts • Why Alerts Matter: Proactively identify and address issues. • Steps to Configure Alerts: • Access Azure Monitor. • Define conditions (e.g., failed pipeline run). • Set notification preferences (e.g., email, SMS).
Common Debugging Scenarios • Data Mapping Issues: Mismatched schemas or missing fields. • Integration Runtime Errors: Incorrect configuration or connectivity issues. • Performance Bottlenecks: Slow transformations or source system delays.
Conclusion and Q&A • Recap: Importance of monitoring and debugging for reliable data pipelines. • Key Takeaways: Leverage Azure’s tools for real-time insights and proactive issue resolution. • Q&A: Open floor for questions.
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