DP203: Azure Data Engineering

Course Description

This course provides the skills needed to design, implement, and manage data management solutions on Azure, using tools such as Azure Data Factory, Azure Synapse Analytics, and Azure Databricks. You will learn to manage complex data pipelines and ensure data security and compliance in a cloud environment.

Learning Objectives

By the end of this course, participants will be able to:

• Understand the concepts of data engineering on Azure.
• Implement data ingestion, transformation, and storage solutions.
• Design and optimize data pipelines for analytical workloads.
• Ensure the security and compliance of data at rest and in transit.
• Monitor and optimize data solutions in production.

Who is this course for?

The course is aimed at a broad audience, including:

• Data Engineers: Responsible for designing and managing data pipelines.
• Cloud Architects: Creating analytical solutions on Azure.
• BI Developers: Looking to leverage Azure's advanced data tools.
• Database Administrators: Migrating or managing data systems in the cloud.
• Data Professionals: Working with large-scale data pipelines.

Prerequisites

No specific prerequisites are required. This course is accessible to anyone wishing to discover data engineering on the Azure Cloud. However, basic knowledge of IT or information systems can be an asset.

Course Syllabus

Day 1: Introduction to Data Engineering on Azure

• Overview of Azure services for data management.
• Data ingestion concepts: batch and real time.
• Demo: Create a simple pipeline with Azure Data Factory.

Day 2: Data Transformation and Processing

• Develop ETL/ELT solutions with Azure Data Factory.
• Integration with Azure SQL Database and Synapse Analytics.
• Hands-on lab: End-to-end ETL pipeline.

Day 3: Security, Governance, and Optimization

• Implementation of security strategies: encryption, access control.
• Introduction to Azure Purview for data governance.
• Demo: Configure governance on a dataset.
• Monitoring pipelines with Azure Monitor and Log Analytics.
• Performance optimization of analytical solutions.
• Recap: Preparation for the DP-203 exam and feedback on hands-on labs.

Course Highlights

• Teaching approach: Alternating theory and practice for better mastery of concepts.
• Qualified instructors: Trainers with concrete experience in the Azure data field.
• Tools and learning materials: Access to online resources, live demonstrations, and real case studies.
• Accessibility: Training open to all, with no advanced technical prerequisites.

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Teaching Methods and Tools Used

• Live demonstrations: Interaction with data engineering services on the Azure cloud.
• Hands-on workshops and real case studies: Applying concepts across various sectors.
• Feedback: Sharing best practices and common pitfalls in companies.
• Simulations and tools: Use Azure data engineering resources in interactive workshops.

Assessment

• Multiple-choice quiz at the end of the course.
• Practical case studies.
• Continuous assessment with personalized feedback.

Normative References

• Azure Well-Architected Framework
• ISO/IEC 19086
• GDPR (General Data Protection Regulation)
• NIST Cloud Computing Standards (SP 500-292)
• ISO 27001 Information Security