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Microsoft

Data Factory & Orchestration

Microsoft via Coursera

Overview

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Advance your automation capabilities by building reliable, production-ready data pipelines in Microsoft Fabric. This hands-on course focuses on pipeline engineering using Fabric Data Factory and Spark orchestration to help you transform raw data into trusted, analytics-ready assets. You’ll create reusable transformations in Dataflows Gen2 using Power Query and schedule refreshes for business users. You’ll design Fabric Data Pipelines with parameters, triggers, and structured error-handling logic, and configure Copy Jobs to ingest incremental CSV and Parquet files from cloud storage. Moving into code-based processing, you’ll author PySpark notebooks for multi-step transformations with optimized Delta outputs, implement data quality validation rules to prevent bad loads, and monitor pipeline, notebook, and job logs to diagnose and resolve failures efficiently. By the end of the course, you’ll be equipped to automate complex data workflows, enforce quality controls, and orchestrate Spark jobs with performance monitoring—developing the engineering discipline required to deliver scalable, enterprise-grade data pipelines.

Syllabus

  • Building Reusable Dataflows with Power Query
    • Create Dataflows Gen2 with Power Query transformations, reusable M functions, and scheduled refreshes that enable self-service analytics.
  • Multi-Step PySpark Transformations
    • Write PySpark notebooks that handle complex transformation logic including joins, aggregations, and business rules, outputting optimized Delta tables.
  • Building Pipelines with Activities and Parameters
    • Create parameterized pipelines with multiple activities for flexible, reusable data ingestion.
  • Triggers and Error Handling
    • Schedule pipeline execution and handle failures gracefully with retry logic and notifications.
  • Configuring Copy Jobs for Incremental Ingestion
    • Set up Copy Jobs for continuous incremental ingestion from cloud storage sources.
  • Implementing Quality Gates in Pipelines
    • Build quality validation into pipelines with rules, quarantine logic, and quality scoring.
  • Diagnosing and Fixing Workflow Failures
    • Systematically diagnose workflow failures using logs and implement targeted fixes.
  • AI-Assisted Pipeline Development
    • Leverage generative AI to accelerate pipeline development, debug failures, and generate optimization suggestions—while understanding when human judgment remains essential.
  • Automated Data Pipeline Project
    • Integrate all Long Course 3 skills to build and document a production-ready automated data pipeline with quality controls and notebook-based transformations.

Taught by

Microsoft

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