Data Engineering on Microsoft Azure Training

Offered to the Jacksonville and Northeast Florida community through UNF Professional and Lifelong Learning in partnership with Applied Technology Academy — live online or in person, taught by ATA's practitioner instructors.

Course Overview

• Design and implement data storage solutions on Azure
• Build and manage data pipelines for batch and streaming data
• Integrate and transform data using Azure Data Factory and Synapse Analytics
• Implement security and compliance features for data solutions
• Monitor, troubleshoot, and optimize data processing systems
• Work effectively with Azure Databricks, Data Lake, and related services
• Identify design decisions to ensure security and recoverability (disaster recovery)

Course Outline

• Module 1: Introduction to Data Engineering on Azure
  • Overview of Azure data services and architecture
  • Roles and responsibilities of a Data Engineer
  • Core principles of data engineering in the cloud
• Module 2: Designing and Implementing Data Storage
  • Choosing the right data storage option
  • Implementing Azure Data Lake Storage Gen2
  • Managing and securing data in Azure storage solutions
• Module 3: Data Ingestion and Integration
  • Designing data ingestion strategies
  • Building pipelines with Azure Data Factory
  • Working with event-based and streaming data ingestion
• Module 4: Data Transformation and Processing
  • Using Azure Synapse Analytics for ETL/ELT
  • Data transformation using Azure Databricks
  • Working with structured and unstructured data
• Module 5: Designing and Developing Data Solutions
  • Implementing batch and real-time data processing
  • Managing data pipelines and orchestration
  • Handling data partitioning and performance tuning
• Module 6: Securing and Monitoring Data Solutions
  • Applying data security and governance
  • Implementing data masking and encryption
  • Monitoring and troubleshooting data pipelines
• Module 7: Performance Optimization and Cost Management
  • Optimizing queries and storage performance
  • Managing cost through data lifecycle policies
  • Automating performance tuning and scaling
• Module 8: Preparing for the DP-203 Exam
  • Review of key concepts and best practices
  • Sample questions and hands-on exercises
  • Exam tips and study strategies

Intended Audience

• Data Engineers
• Data Architects
• Business Intelligence (BI) Professionals

Prerequisites

• Basic understanding of cloud concepts and Azure services
• Experience with data processing languages such as SQL or Python
• Familiarity with core data concepts (relational and non-relational data)