ADF Monitoring & Logging
Azure Cloud Data Engineering Training in Hyderabad – Quality Thoughts
Quality Thoughts offers one of the best Azure Cloud Data Engineering courses in Hyderabad, ideal for graduates, postgraduates, working professionals, or career switchers. The course combines hands-on learning with an internship to make you job-ready in a short time.
Our expert-led training goes beyond theory, with real-time projects guided by certified cloud professionals. Even if you’re from a non-IT background, our structured approach helps you smoothly transition into cloud roles.
The course includes labs, projects, mock interviews, and resume building to enhance placement success.
Why Choose Us?
1. Live Instructor-Led Training
2. Real-Time Internship Projects
3.Resume & Interview Prep
4 .Placement Assistance
5.Career Transition Support
Join us to unlock careers in cloud data engineering. Our alumni work at top companies like TCS, Infosys, Deloitte, Accenture, and Capgemini.
Note: Azure Table and Queue Storage support NoSQL and message handling for scalable cloud apps
ADF Monitoring & Logging
Azure Data Factory (ADF) provides robust monitoring and logging capabilities to help track data movement, transformation, and pipeline execution. These tools allow developers and administrators to diagnose issues, analyze performance, and ensure data pipeline health.
The Monitoring Hub in ADF Studio offers a graphical interface to view pipeline runs, trigger history, activity runs, and debug sessions. Users can filter by pipeline name, status (Succeeded, Failed, Cancelled), time range, and more. Each pipeline run provides a detailed view of all executed activities, duration, input/output data, and any error messages.
Activity Runs show execution status for each step, including success/failure, error codes, and messages. You can retry failed activities directly from the interface.
ADF integrates with Azure Monitor, Log Analytics, and Azure Application Insights for advanced logging, metrics, and alerting. Logs include diagnostics, integration runtime usage, activity run details, and performance metrics. You can configure diagnostic settings to send logs to a Log Analytics workspace for querying via Kusto Query Language (KQL).
Alerts can be set based on metrics like pipeline failures, duration thresholds, or activity status. This enables proactive issue resolution.
Overall, ADF’s monitoring and logging tools help maintain pipeline reliability, audit data processes, and meet enterprise data governance and compliance needs.
Read More
Copy Activity (Blob to SQL etc.)
Mapping & Wrangling Data Flows
Visit Our Website
Quality Thought Institute in Hyderabad
Comments
Post a Comment