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Advanced Data Management in Azure Databricks · LearnSpace
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courseraIT и технологии

Advanced Data Management in Azure Databricks

Курс от Packt
Продвинутый≈ 12.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This advanced course on Azure Databricks will empower you with the skills to manage complex data workflows efficiently. With a focus on advanced features like Unity Catalog, Delta Tables, and Databricks Ingestion Tools, you will gain hands-on experience in managing large-scale data pipelines, ensuring data consistency, and implementing data governance across the Databricks platform. By the end of the course, you'll have a comprehensive understanding of Databricks' capabilities in data management, equipping you to handle enterprise-level data solutions. The course begins by introducing Unity Catalog, showing how it can be set up and used for managing user access and securing objects in your Databricks environment. You’ll learn how to configure the Unity Catalog and work with various securable objects, ensuring a secure and organized data landscape. As you progress, you will dive deeper into Delta Lake and Delta Tables, starting with an introduction to Delta Lake's features, followed by a thorough exploration of how to create and manage Delta Tables, including reading and optimizing them for performance. In the later modules, you’ll explore Databricks' incremental ingestion tools. You will be introduced to the architecture and use cases of incremental data ingestion, including how to leverage tools like Copy Into and Databricks Autoloader with schema evolution. You’ll also work with streaming data ingestion to ensure real-time data processing with minimal effort. The course concludes with an introduction to Delta Live Tables (DLT), where you’ll learn to create DLT pipelines and workloads using SQL and Python, solidifying your knowledge in streamlining real-time analytics. This course is ideal for experienced data engineers, data architects, and data scientists who want to specialize in Azure Databricks. Prior experience with cloud-based data platforms, SQL, and Python is recommended. With a focus on practical application, this course is designed to take your expertise in data management to the next level.

Навыки, которые вы освоите

Data ManagementDatabricksData PipelinesUser ProvisioningPerformance TuningData GovernanceReal Time DataData Import/ExportMicrosoft AzureData IntegrationMetadata ManagementData Lakes

Программа курса

4 модулей · 45 учебных материалов

01Working with Unity Catalog9 материалов

Working with Unity Catalog

Introduction to the Course 'Advanced Data Management in Azure Databricks'ЧтениеFull Specialization ResourcesЧтениеWhat will you learn in this sectionВидеоIntroduction to Unity CatalogВидео

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Packt - Course Instructors

Преподаватель курса

Advanced Data Management in Azure Databricks
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 12.7 ч

4 модулей

Язык: Английский

Субтитры: Казахский

Часть программы вашего университета
Setup Unity CatalogВидео
Unity Catalog User ProvisioningВидео
Working with Securable ObjectsВидео
Understanding Databricks Unity CatalogDIALOGUE
Working with Unity Catalog - AssessmentЗадание
02Working with Delta Lake and Delta Tables14 материалов

Working with Delta Lake and Delta Tables

What will you learn in this sectionВидеоIntroduction to Delta LakeВидеоCreating Delta TableВидеоSharing data for External Delta TableВидеоReading Delta TableВидеоDelta Table OperationsВидеоDelta Table Time TravelВидеоConvert Parquet to DeltaВидеоDelta Table Schema ValidationВидеоDelta Table Schema EvolutionВидеоLook Inside Delta TableВидеоDelta Table Utilities and OptimizationВидеоSchema Evolution in Delta LakeDIALOGUEWorking with Delta Lake and Delta Tables - AssessmentЗадание
03Working with Databricks Incremental Ingestion Tools10 материалов

Working with Databricks Incremental Ingestion Tools

What will you learn in this sectionВидеоArchitecture and Need for Incremental IngestionВидеоUsing Copy Into with Manual Schema EvolutionВидеоUsing Copy Into with Automatic Schema EvolutionВидеоStreaming Ingestion with Manual Schema EvolutionВидеоStreaming Ingestion with Automatic Schema EvolutionВидеоIntroduction to Databricks AutoloaderВидеоAutoloader with Automatic Schema EvolutionВидеоIncremental Data Ingestion in Lakehouse ArchitectureDIALOGUEWorking with Databricks Incremental Ingestion Tools - AssessmentЗадание
04Working with Databricks Delta Live Tables (DLT)12 материалов

Working with Databricks Delta Live Tables (DLT)

What will you learn in this sectionВидеоIntroduction to Databricks DLTВидеоUnderstand DLT Use Case ScenarioВидеоSetup DLT Scenario DatasetВидеоCreating DLT Workload in SQLВидеоCreating DLT Pipeline for your WorkloadВидеоCreating DLT Workload in PythonВидеоConclusion to the Course 'Advanced Data Management in Azure Databricks'ЧтениеUnderstanding Delta Live Tables (DLT)DIALOGUEWorking with Databricks Delta Live Tables (DLT) - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание