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Build Data Lakes and Data Warehouses on Google Cloud · LearnSpace
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courseraIT и технологии

Build Data Lakes and Data Warehouses on Google Cloud

Курс от Google Cloud
Средний≈ 5.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.

Навыки, которые вы освоите

Google Cloud PlatformData LakesData WarehousingData ArchitectureData GovernanceCloud StorageData SecurityBig DataData IntegrationMachine Learning MethodsData ProcessingAnalyticsCloud Computing ArchitectureData InfrastructureAdvanced AnalyticsData Management

Программа курса

7 модулей · 33 учебных материалов

01Course introduction1 материалов

Course Introduction

Course IntroductionPLUGIN
02Introduction to Modern Data Engineering on Google Cloud5 материалов

Introduction to Modern Data Engineering on Google Cloud

Module 1: Introduction to modern data engineering on Google CloudPLUGINThe classics: Data lakes and data warehousesPLUGIN

Учитесь у экспертов

Google Cloud Training

Преподаватель курса

Build Data Lakes and Data Warehouses on Google Cloud
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 5.4 ч

7 модулей

Язык: Английский

Часть программы вашего университета
The modern approach: Data lakehousePLUGIN
Choosing the right architecturePLUGIN
Data warehouses, lakes, and lakehouses conceptsЗадание
03Building a Data Lakehouse with Cloud Storage, Open Formats, and BigQuery10 материалов

Building a Data Lakehouse with Cloud Storage, Open Formats, and BigQuery

Module 2: Building a data lakehouse with Cloud Storage, open formats, and BigQueryPLUGINBuilding a data lake foundationPLUGINIntroduction to Apache Iceberg open table formatPLUGINBigQuery as the central processing enginePLUGINCombining operational data in AlloyDBPLUGINCombining operational and analytical data with federated queriesPLUGINReal world use casePLUGINData lakehouse features and benefitsЗаданиеAccessing and completing labsPLUGINFederated query with BigQuery and AlloyDBВнешний инструмент
04Modernizing Data Warehouses with BigQuery and Lakehouse6 материалов

Modernizing Data Warehouses with BigQuery and BigLake

Module 3: Modernizing data warehouses with BigQuery and LakehousePLUGINBigQuery fundamentalsPLUGINPartitioning and clustering in BigQueryPLUGINIntroducing Lakehouse and external tablesPLUGINQuerying external data and Iceberg tablesВнешний инструментBigQuery and LakehouseЗадание
05Advanced Lakehouse Patterns and Data Governance6 материалов

Advanced Lakehouse Patterns and Data Governance

Module 4: Advanced lakehouse patterns and data governancePLUGINData governance and security in a unified platformPLUGINDemo: Data Loss PreventionPLUGINAnalytics and machine learning on the lakehousePLUGINReal-world lakehouse architectures and migration strategiesPLUGINSecurity and ML in the lakehouseЗадание
06Labs and Best Practices4 материалов

Labs and Best Practices

Module 5: Labs and best practicesPLUGINGetting Started with BigQuery MLВнешний инструментVector search with BigQueryВнешний инструментReview and best practicesPLUGIN
07Course Summary1 материалов

Summary and next steps

Summary and next stepsPLUGIN