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Data Factory & Orchestration · LearnSpace
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Data Factory & Orchestration

Курс от Microsoft
Продвинутый≈ 7.9 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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.

Навыки, которые вы освоите

Data TransformationData ValidationData PipelinesCloud StorageData QualityData Import/ExportPySparkDebuggingDataflowApache SparkData LakesMicrosoft Power PlatformData ProcessingData ArchitectureAI WorkflowsGenerative AIData ManipulationData GovernanceExtract, Transform, Load

Программа курса

9 модулей · 54 учебных материалов

01Building Reusable Dataflows with Power Query6 материалов
Why No-Code Transforms Change the GameВидеоDataflows Gen2 and Power Query FundamentalsВидеоM Function Patterns and Scheduling ReferenceЧтениеGUIDED PRACTICE: Build a Reusable DataflowЧтение

Учитесь у экспертов

Microsoft

Преподаватель курса

Data Factory & Orchestration
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 7.9 ч

9 модулей

Язык: Английский

Часть программы вашего университета
Building Reusable Dataflows with Power QueryЗадание
Create No-Code Dataflows AssessmentЗадание
02Multi-Step PySpark Transformations7 материалов
Discover When Code Beats Visual ToolsDIALOGUEPySpark Fundamentals and Transformation PatternsВидеоTransformation Patterns and Delta Write ReferenceЧтениеHands-on Activity: Write Multi-Step Transformation NotebookЧтениеHands-on Activity: Apply Multi-Step Transformations to Regional Sales DataЗаданиеMulti-Step PySpark TransformationsЗаданиеTransform Data with PySpark AssessmentЗадание
03Building Pipelines with Activities and Parameters6 материалов
Why Manual Processes FailВидеоPipeline Activities and Parameters - Part 1ВидеоPipeline Activities and Parameters - Part 2ВидеоExpression Reference and PatternsЧтениеHands-on Activity: Build a Parameterized PipelineЧтениеBuilding Pipelines with Activities and ParametersЗадание
04Triggers and Error Handling6 материалов
When Pipelines Fail at 3 AMЧтениеTriggers, Retries, and Failure HandlingВидеоError Handling PatternsЧтениеHands-on Activity: Add Triggers and Error HandlingЧтениеTriggers and Error HandlingЗаданиеAutomate Pipelines Daily AssessmentЗадание
05Configuring Copy Jobs for Incremental Ingestion6 материалов
Continuous Ingestion Without Pipeline ComplexityВидеоCopy Job Configuration and ModesВидеоCopy Job Configuration ReferenceЧтениеGuided Practice: Configure Incremental Copy JobЧтениеConfiguring Copy Jobs for Incremental IngestionЗаданиеMaster Copy Jobs Ingestion AssessmentЗадание
06Implementing Quality Gates in Pipelines7 материалов
When Bad Data Poisons EverythingВидеоQuality Rules, Quarantine, and ScoringВидеоQuality Rule Patterns and Code ExamplesЧтениеHands-on Activity: Design Quality Validation RulesЗаданиеHands-on Activity: Integrate Quality Gate into PipelineЧтениеImplementing Quality Gates in PipelinesЗаданиеGuard Quality in Pipelines AssessmentЗадание
07Diagnosing and Fixing Workflow Failures7 материалов
The 3 AM AlertВидеоReading Pipeline, Notebook, and Copy Job LogsВидеоError Pattern Reference GuideЧтениеHands-on Activity: Diagnose a Failed PipelineЗаданиеEscalate to On-Call LeadDIALOGUEDiagnosing and Fixing Workflow FailuresЗаданиеDebug Fabric Workflows Fast AssessmentЗадание
08AI-Assisted Pipeline Development5 материалов
AI as Your Pipeline Development PartnerЧтениеAI Tools and Pipeline Use CasesЧтениеAI Limitations and Validation RequirementsЧтениеAI-Assisted Problem SolvingDIALOGUEAI-Assisted Pipeline DevelopmentЗадание
09Automated Data Pipeline Project4 материалов
Why This Project MattersЧтениеProject Requirements and Evaluation CriteriaЧтениеProject InstructionsЧтениеAutomated Data Pipeline Document ProjectЗадание