Orchestrating Data Pipelines in Microsoft Fabric

Microsoft via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 2 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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This course focuses on the orchestration, automation, and monitoring of data engineering workflows within Microsoft Fabric. After datasets have been ingested, transformed, and structured for analytics, data engineers must coordinate how those processes run reliably across enterprise environments. Learners examine how Microsoft Fabric pipelines orchestrate multi-step data workflows, automate scheduled processing tasks, and coordinate dependencies between ingestion, transformation, and analytics operations. The course also introduces real-time data engineering concepts through streaming architectures, demonstrating how continuous data sources such as telemetry or operational events can be integrated into Fabric environments. Finally, learners explore monitoring and troubleshooting practices used to maintain reliable data pipelines. By understanding how engineers observe pipeline execution, identify failures, and diagnose performance issues, learners develop the operational skills required to maintain production data engineering systems. By the end of the course, learners understand how Fabric pipelines coordinate complex workflows, how streaming architectures support real-time data processing, and how engineers monitor and troubleshoot data engineering systems in production environments.

Syllabus

Pipeline orchestration

This module introduces pipeline orchestration within Microsoft Fabric and explains how data engineers coordinate multi-step data workflows across ingestion, transformation, and analytics systems. You will examine how Fabric pipelines allow engineers to define sequences of data processing activities that run automatically according to defined dependencies. These workflows ensure that datasets move reliably through the data engineering lifecycle without requiring manual intervention. The module focuses on the concept of orchestration rather than complex pipeline design. You will explore how pipelines coordinate tasks such as running ingestion workflows, triggering transformation processes, and ensuring datasets are prepared before analytics queries execute. By understanding how orchestration coordinates multiple stages of the data engineering lifecycle, you will develop the operational perspective required to manage reliable data pipelines in enterprise environments.

Workflow automation and scheduling

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.

Streaming data architectures

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.

Monitoring and troubleshooting

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.