This course is designed to provide participants with a comprehensive understanding of data engineering concepts and practices within the Azure cloud ecosystem.

Through hands-on projects and real-world case studies, participants will gain practical skills in designing, building, and maintaining data solutions on Azure.

Module 1: Introduction to Data Engineering
– Overview of data engineering concepts and roles
– Importance of data engineering in modern data solutions
– Introduction to Azure Cloud services
– Understanding data lifecycle management

Module 2: Azure Fundamentals
– Overview of Microsoft Azure
– Azure account creation and subscription management
– Azure portal navigation
– Understanding Azure Resource Manager (ARM)

Module 3: Azure Data Services Overview
– Introduction to Azure Storage (Blob, Table, Queue)
– Overview of Azure SQL Database and Azure Cosmos DB
– Introduction to Azure Data Lake Storage
– Understanding Azure Synapse Analytics

Module 4: Data Ingestion and ETL Processes
– Introduction to ETL (Extract, Transform, Load) concepts
– Using Azure Data Factory for data ingestion
– Creating and scheduling data pipelines
– Data flow transformations
– Ingesting data from various sources (on-premises, cloud, etc.)

Module 5: Data Transformation and Processing
– Using Azure Databricks for data processing
– Introduction to Apache Spark on Azure
– Data transformation using PySpark and Scala
– Working with Azure Functions for serverless data processing

Module 6: Data Modeling and Warehousing
– Introduction to data modeling concepts
– Designing data models in Azure Synapse Analytics
– Implementing a data warehouse solution in Azure
– Best practices for data warehousing on Azure

Module 7: Data Security and Compliance
– Overview of data security in Azure
– Implementing Azure Role-Based Access Control (RBAC)
– Understanding Azure security best practices
– Compliance and governance considerations for data engineering

Module 8: Data Analytics and Visualization
– Introduction to data analytics with Azure
– Using Azure Synapse Analytics for analytics solutions
– Visualizing data with Power BI
– Creating interactive dashboards and reports

Module 9: Real-time Data Processing
– Understanding real-time data processing concepts
– Using Azure Stream Analytics for real-time analytics
– Implementing Azure Event Hubs for data streaming
– Introduction to Azure Functions for real-time data processing

Module 10: Monitoring and Optimization
– Monitoring data pipelines with Azure Monitor
– Best practices for optimizing data solutions on Azure
– Performance tuning in Azure Data Factory and Azure Databricks
– Troubleshooting common data engineering issues

Module 11: Capstone Project
– Designing and implementing a complete data engineering solution on Azure
– End-to-end data pipeline creation from ingestion to visualization
– Presentation of project findings and insights