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Advanced Data Testing for Quality at Scale · LearnSpace
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Advanced Data Testing for Quality at Scale

Курс от Coursera
Продвинутый≈ 3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Advanced ALM Strategies with Azure DevOps and GitHub Integration is an advanced-level course designed for DevOps engineers, release managers, and software delivery leaders who want to implement scalable, secure, and policy-driven Application Lifecycle Management (ALM) practices. Taught by experienced DevOps professionals, this course equips learners with the tools and strategies needed to optimize software delivery pipelines across complex enterprise environments. Through real-world use cases, scenario-based walkthroughs, hands-on activities, and design challenges, learners will explore advanced branching models, secure CI/CD pipelines, automated quality gates, and governance frameworks using GitHub, Azure DevOps, and supporting integrations. You'll learn to build traceable workflows, enforce compliance and testing standards, and evaluate your DevOps maturity using monitoring and feedback loops. By the end of the course, you’ll have designed a personalized ALM blueprint that aligns delivery speed with security, scale, and compliance—ready to apply directly in your organization.

Навыки, которые вы освоите

Data ValidationData GovernanceExtract, Transform, LoadApache AirflowDevOpsSQLAzure DevOpsDevSecOpsApplication Lifecycle ManagementCI/CDContinuous DeliveryData PipelinesAzure DevOps PipelinesContinuous IntegrationData QualityTest Automation

Программа курса

3 модулей · 22 учебных материалов

01Lesson 1: Build Trust by Design: Automating Data Validation at Scale8 материалов
The Dashboard That LiedDIALOGUEWelcome to the Course: Course OverviewЧтениеIntroduction and WelcomeВидеоIntroduction to Automated Data Validation with Great Expectations & SQLЧтение

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Преподаватель курса

Advanced Data Testing for Quality at Scale
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 3 ч

3 модулей

Язык: Английский

Часть программы вашего университета
Why Data Validation Matters—and How to Start with ExpectationsВидео
Organizing, Automating, and Scaling Your Data TestsВидео
HOL (Interactive): Build Your First Expectation Suite with Great ExpectationsЗадание
Evaluating Your Expectation Suite for Real-World ReadinessDIALOGUE
02Lesson 2: Automating Data Quality: CI/CD-Ready Validation in Pipelines6 материалов
Stop Bad Data Early: Data Validation in ETL WorkflowsВидеоETL development life‑cycle with Dataflow–Netflix Technology BlogЧтениеShift Left for Data: Embedding Validation into CI/CD WorkflowsВидеоIntegrating Great Expectations into CI/CD for Robust Pipeline ValidationЧтениеHOL (Interactive): Simulate Automated Data Validation in Your CI/CD or ETL PipelineЗаданиеEvaluating Your Pipeline-Integrated Validation StrategyDIALOGUE
03Lesson 3: Monitoring, Governance & Continuous Improvement in Data Testing8 материалов
From Testing to Trust: Monitoring Data Quality at ScaleВидеоOperationalizing Quality: Governance & Collaboration for Resilient PipelinesВидеоA Guide to Data Governance in Modern Data PipelinesЧтениеHOL (Interactive): Design a Data Quality Governance & Monitoring PlanЗаданиеEvaluating Your Governance and Monitoring StrategyDIALOGUECongratulations and Continuous Learning JourneyВидеоProject: Build Your Enterprise Data Quality Governance BlueprintЗаданиеAssessmentЗадание