This module introduces workflow automation techniques used to manage recurring data engineering tasks within Microsoft Fabric environments. After engineers design pipeline workflows, those pipelines must execute automatically according to schedules or system events in order to support reliable analytics operations. You will examine how scheduling and automation mechanisms allow pipelines to run at defined intervals or in response to specific triggers. These automation capabilities ensure that ingestion, transformation, and dataset preparation tasks occur consistently without manual intervention. The module focuses on the operational configuration of automated data workflows rather than complex pipeline design. You will explore how engineers configure scheduled executions, manage event-based triggers, and monitor automated workflows to ensure that recurring data processing tasks run reliably. By understanding automation and scheduling practices, you will develop the operational awareness required to maintain reliable data engineering systems that support analytics workloads.

This module introduces real-time data engineering workflows and explains how streaming architectures allow organizations to process continuously generated data within Microsoft Fabric environments. You will explore how modern applications and connected systems generate event-based data streams such as device telemetry, application logs, or operational metrics. Unlike batch datasets that are processed at scheduled intervals, streaming data pipelines process events as they occur, enabling near real-time analytics and operational monitoring. The module focuses on the architectural concepts underlying streaming systems rather than advanced distributed systems engineering. You will examine how streaming pipelines ingest event data, process event streams, and deliver structured outputs that can support analytics and monitoring workloads. By understanding how streaming architectures complement traditional batch pipelines, you will develop the conceptual foundation required to build data engineering systems that support both scheduled analytics workloads and real-time operational data processing.

This module introduces the monitoring and troubleshooting practices used to maintain reliable data engineering systems within Microsoft Fabric environments. After data pipelines, automated workflows, and streaming architectures are deployed, engineers must ensure that these systems operate consistently and respond appropriately when failures occur. You will explore how monitoring tools provide visibility into pipeline execution, workflow status, and system performance. By examining pipeline run histories, execution logs, and workflow outcomes, engineers can identify processing failures, diagnose issues, and restore data pipeline functionality. The module focuses on operational visibility rather than complex debugging techniques. You will examine how monitoring dashboards and execution histories help engineers confirm that data pipelines run successfully and how troubleshooting workflows allow engineers to identify and correct errors when failures occur. By understanding how monitoring and troubleshooting practices support reliable data engineering systems, you will develop the operational awareness required to maintain production data workflows in Microsoft Fabric environments.