CLOUD / DATA LEARNING PATH
Design data movement and orchestration flows using pipelines, activities, and linked services.
Azure Data Factory is a data integration service. This path introduces pipeline design, connections, triggers, parameters, monitoring, and practical recovery patterns.
Explore Azure Data Factory through beginner, intermediate, and advanced learning levels. Topic status is illustrative demo progress only.
Build fluency with the core ideas and everyday building blocks.
Understand pipeline structure and activity flow.
Represent connections and data locations.
Combine concepts into reliable, useful workflows.
Plan movement and transformation steps.
Reuse pipeline logic and schedule execution.
Understand connectivity and execution environments.
Reason about scale, trade-offs, security, and optimization.
Inspect runs, failures, and recovery behavior.
Organize configuration-driven integration patterns.
Status labels are sample progress and are not linked to a user account.
Try focused learning activities that reinforce the Azure Data Factory concepts you have just explored.
Use a project to connect technical concepts to a practical problem and its constraints.
Demo project outline · Azure Data Factory learning application
Explore projectsDemo project outline · Azure Data Factory learning application
Explore projectsReview your understanding and practice explaining your choices in a clear, structured way.
Recover a failed scheduled pipeline
Handle changing source schemas
Design a safe incremental load
A real learner dashboard could track progress by topic and stage when user accounts are introduced.
Demo Progress · not user data