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The Path to Insights: Data Models and Pipelines · LearnSpace
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The Path to Insights: Data Models and Pipelines

Курс от Google
Продвинутый≈ 17.2 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This is the second of four courses in the Google Business Intelligence Certificate. In this course, you'll explore data modeling and how databases are designed. Then you’ll learn about extract, transform, load (ETL) processes that extract data from source systems, transform it into formats that enable analysis, and drive business processes and goals. Google employees who currently work in BI will guide you through this course by providing hands-on activities that simulate job tasks, sharing examples from their day-to-day work, and helping you build business intelligence skills to prepare for a career in the field. Learners who complete the four courses in this certificate program will have the skills needed to apply for business intelligence jobs. This certificate program assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Determine which data models are appropriate for different business requirements -Describe the difference between creating and interacting with a data model -Create data models to address different types of questions -Explain the parts of the extract, transform, load (ETL) process and tools used in ETL -Understand extraction processes and tools for different data storage systems -Design an ETL process that meets organizational and stakeholder needs -Design data pipelines to automate BI processes

Навыки, которые вы освоите

Extract, Transform, LoadData TransformationDatabase SystemsDatabase DesignData QualityData MartDatabasesData ValidationData ModelingData PipelinesData IntegrityBusiness IntelligenceData ManagementData WarehousingPerformance TestingData IntegrationBusiness Process

Программа курса

4 модулей · 112 учебных материалов

01Data models and pipelines46 материалов

Get started with data modeling, schemas, and databases

Introduction to Course 2ВидеоHelpful resources and tipsЧтениеEd: Overcome imposter syndromeВидеоCourse 2 overviewЧтение

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Преподаватель курса

The Path to Insights: Data Models and Pipelines
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Обучение на Coursera

≈ 17.2 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Украинский, Бразильский португальский, Корейский, Немецкий, Индонезийский, Турецкий, Испанский, Японский

Часть программы вашего университета
Welcome to module 1Видео

Discover data models and schemas

Data modeling, design patterns, and schemasВидеоGet the facts with dimensional modelsВидеоDimensional models with star and snowflake schemasВидеоDesign efficient database systems with schemasЧтениеChoose the best schemaЗаданиеDifferent data types, different databasesВидеоDatabase comparison checklistЧтениеTest your knowledge: Data modeling, schemas, and databasesЗадание

Choose the right database

The shape of the dataВидеоDesign useful database schemasВидеоFour key elements of database schemasЧтениеReview a database schemaЧтениеInspect: Database models and schemasPLUGINTest your knowledge: Choose the right databaseЗадание

How data moves

Data pipelines and the ETL processВидеоTransport: More about the data pipelinePLUGINMaximize data through the ETL processВидеоChoose the right tool for the job ВидеоBusiness intelligence tools and their applicationsЧтениеETL-specific tools and their applicationsЧтениеTest your knowledge: How data movesЗадание

Data-processing with Dataflow

Introduction to DataflowВидео[Optional] Activity: Create a Google Cloud accountЗаданиеGuide to DataflowЧтение[Optional] Activity: Create a streaming pipeline in DataflowЗаданиеCoding with PythonВидеоPython applications and resourcesЧтение

Organize data in BigQuery

Gather information from stakeholdersВидеоMerge data from multiple sources with BigQueryЧтениеActivity: Set up a sandbox and query a public dataset in BigQueryЗаданиеUnify data with target tablesЧтениеActivity: Create a target table in BigQueryЗаданиеActivity Exemplar: Create a target table in BigQueryЧтениеCase study: Wayfair - Working with stakeholders to create a pipelineЧтение

Review: Data models and pipelines

Wrap-upВидеоGlossary terms from course 2, module 1ЧтениеModule 1 challengeЗадание

[Optional] Review Google Data Analytics Certificate content

[Optional] Review Google Data Analytics Certificate content about data typesВидео[Optional] Review Google Data Analytics Certificate content about primary and foreign keysВидео[Optional] Review Google Data Analytics Certificate content about BigQuery Видео[Optional] Review Google Data Analytics Certificate content about SQL best practicesЧтение
02Dynamic database design19 материалов

Database performance

Welcome to module 2ВидеоData marts, data lakes, and the ETL processВидеоETL versus ELTЧтениеThe five factors of database performanceВидеоA guide to the five factors of database performanceЧтениеCoach dialogue: Identify the five factors of database performanceDIALOGUEOptimize database performanceВидеоIndexes, partitions, and other ways to optimizeЧтениеActivity: Partition data and create indexes in BigQueryЗаданиеActivity Exemplar: Partition data and create indexes in BigQueryЧтениеStore: Understand data storage systemsPLUGINCase study: Deloitte - Optimizing outdated database systemsЧтениеThe five factors in actionВидеоDetermine the most efficient queryЧтениеDesign: Optimize for database speedPLUGINTest your knowledge: Database performanceЗадание

Review: Dynamic database design

Wrap-upВидеоGlossary terms from course 2, module 2ЧтениеModule 2 challengeЗадание
03Optimize ETL processes27 материалов

Optimizing pipelines and ETL processes

Welcome to module 3ВидеоThe importance of quality testingВидеоSeven elements of quality testingЧтениеValidate: Data quality and integrityPLUGINMonitor data quality with SQLЧтениеMana: Quality data is useful dataВидеоTest your knowledge: Optimize pipelines and ETL processesЗадание

Data schema validation

Conformity from source to destinationВидеоSample data dictionary and data lineageЧтениеCheck your schemaВидеоSchema-validation checklistЧтениеActivity: Evaluate a schema using a validation checklist ЗаданиеActivity Exemplar: Evaluate a schema using a validation checklistЧтение

Business rules and performance testing

Verify business rulesВидеоBusiness rulesЧтениеDatabase performance testing in an ETL contextЧтениеEvaluate: Performance test your data pipelinePLUGINDefend against known issuesЧтениеBurak: Evolving technologyВидео

Review: Optimize ETL processes

Wrap-upВидеоGlossary terms from course 2, module 3ЧтениеModule 3 challengeЗадание

[Optional] Review Google Data Analytics Certificate content

[Optional] Review Google Data Analytics Certificate content about data integrityВидео[Optional] Review Google Data Analytics Certificate content about metadataВидео
04Course 2 end-of-course project20 материалов

Apply your skills to a workplace scenario

Welcome to module 4ВидеоContinue your end-of-course projectВидеоExplore Course 2 end-of-course project scenariosЧтение

Cyclistic scenario

Course 2 workplace scenario overview: CyclisticЧтениеCyclistic datasetsЧтениеObserve the Cyclistic team in actionЧтениеActivity: Create your target table for CyclisticЗаданиеActivity Exemplar: Create your target table for CyclisticЧтение

Google Fiber scenario

Course 2 workplace scenario overview: Google FiberЧтениеGoogle Fiber datasetsЧтение[Optional] Merge Google Fiber datasets in TableauЧтениеActivity: Create your target table for Google FiberЗаданиеActivity Exemplar: Create your target table for Google FiberЧтение

End-of-course project wrap-up

Tips for ongoing success with your end-of-course projectВидеоLuis: Tips for interview preparationВидеоAssess your Course 2 end-of-course projectЗадание

Course review: The Path to Insights: Data Models and Pipelines

Reflect and connect with peersЧтениеCourse wrap-upВидеоCourse 2 glossaryЧтениеGet started on Course 3Чтение
Test your knowledge: Data schema validationЗадание
Case study: FeatureBase, Part 2: Alternative solutions to pipeline systemsЧтение
Test your knowledge: Business rules and performance testing Задание