Upskill your career with the Microsoft Azure Data Engineering Certification Training course.
- Designed by industry professionals to prepare you for the DP-203 Certification exam.
- Covers key tools and services such as Azure Data Factory, Data Warehouse, Azure Databricks, Azure Synapse Analytics, and Azure HD Insight.
- Access scenario-based exam dumps for comprehensive exam preparation.
- Gain practical, hands-on experience with capstone projects and real-time applications.
Curriculum
- 5 Sections
- 71 Lessons
- 10 Weeks
Expand all sectionsCollapse all sections
- Introduction to Microsoft Azure and its Services7
- Introduction To Azure Data Engineering14
- 2.0Understand the evolving world of data
- 2.1Data abundance
- 2.2Understanding the Data Engineering Problem
- 2.3Understand job responsibilities
- 2.4Understanding Data Engineering Processing – Extract Transform and Load
- 2.5Overview of Azure Data Engineering Services
- 2.6Understand data storage in Azure Storage
- 2.7Understand data storage in Azure Data Lake Storage
- 2.8Understand Azure Cosmos DB
- 2.9Understand Azure SQL Database
- 2.10Understand Azure Synapse Analytics
- 2.11Understand Azure Stream Analytics
- 2.12Understand Azure HDInsight
- 2.13Understand other Azure data services
- Storing Data in Azure13
- 3.0How to choose an Azure Storage Service in Azure
- 3.1Create an Azure Storage Account
- 3.2Connect an app to Azure Storage API
- 3.3Connect to your Azure storage account
- 3.4Explore Azure Storage security features
- 3.5Understand storage account keys
- 3.6Understand shared access signatures
- 3.7Control network access to your storage account
- 3.8Understand Advanced Threat Protection for Azure Storage
- 3.9Explore Azure Data Lake Storage security features
- 3.10Introduction to Blob storage
- 3.11What are blobs?
- 3.12Design a storage organization strategy
- Azure Data Factory - I15
- 4.0Integrate data with Azure Data Factory or Azure Synapse Pipeline
- 4.1Understand Azure Data Factory
- 4.2Describe data integration patterns
- 4.3Explain the data factory process
- 4.4Understand Azure Data Factory components
- 4.5Azure Data Factory security
- 4.6Set-up Azure Data Factory
- 4.7Create linked services
- 4.8Create datasets
- 4.9Create data factory activities and pipelines
- 4.10Manage integration runtimes
- 4.11Petabyte-scale ingestion with Azure Data Factory or Azure Synapse Pipeline
- 4.12List the data factory ingestion methods
- 4.13Describe data factory connectors
- 4.14Understand data ingestion security considerations
- Azure Data Factory - II22
- 5.0Explain Data Factory transformation methods
- 5.1Describe Data Factory transformation types
- 5.2Debug mapping data flow
- 5.3Describe slowly changing dimensions
- 5.4Choose between slowly changing dimension types
- 5.5Understand data factory control flow
- 5.6Work with data factory pipelines
- 5.7Debug data factory pipelines
- 5.8Add parameters to data factory components
- 5.9Execute data factory packages
- 5.10Describe SQL Server Integration Services
- 5.11Understand the Azure-SIS integration runtime
- 5.12Set-up Azure-SIS integration runtime
- 5.13Run SSIS packages in Azure Data Factory
- 5.14Migrate SSIS packages to Azure Data Factory
- 5.15Configure a git repository with a development factory
- 5.16Create and merge a feature branch
- 5.17Deploy a release pipeline
- 5.18Visually monitor pipeline runs
- 5.19Integrate with Azure Monitor
- 5.20Set up alerts
- 5.21Rerun pipeline runs
