DP-203 Azure Data Engineering Training
A practical, instructor-led training program for professionals who want to design, build, and operate modern data engineering solutions on Microsoft Azure.
Course Overview
This course focuses on the core skills required to implement data engineering solutions using Azure services. Participants learn how to ingest, transform, store, secure, monitor, and serve data for analytics and business intelligence workloads.
The training combines architecture discussions, live demonstrations, guided labs, and real-world implementation scenarios. It is suitable for professionals preparing for the DP-203 certification exam as well as teams building modern cloud data platforms.
What You Will Learn
- Design and implement modern cloud data engineering solutions on Azure
- Build data ingestion and transformation pipelines
- Work with data lakes, lakehouse architectures, and analytical stores
- Use Azure Data Factory and Synapse pipelines for ETL and ELT workloads
- Understand Azure Synapse Analytics, Dedicated SQL Pool, and Serverless SQL concepts
- Apply security, monitoring, performance, and governance best practices
- Prepare for Microsoft DP-203 certification topics through practical examples
Target Audience
- Data engineers
- Database professionals
- BI developers and analytics professionals
- Cloud engineers working with data platforms
- Developers transitioning into data engineering roles
- IT teams planning or implementing Azure data solutions
Prerequisites
- Basic understanding of databases and SQL
- General knowledge of cloud computing concepts
- Experience with data analysis, reporting, or application data is helpful
- No advanced Azure experience is required, but basic Azure familiarity is beneficial
Detailed Syllabus
- Modern data platform concepts and Azure data architecture patterns
- Azure storage options for data engineering workloads
- Azure Data Lake Storage Gen2 and data lake design principles
- Data ingestion patterns: batch, incremental, and scheduled pipelines
- Azure Data Factory pipelines, linked services, datasets, and triggers
- Data transformation approaches using mapping data flows and SQL-based ELT
- Azure Synapse Analytics overview and workspace architecture
- Dedicated SQL Pool concepts, distribution, partitioning, and performance considerations
- Serverless SQL Pool for querying files in the data lake
- Apache Spark concepts for large-scale data transformation
- Microsoft Fabric, Lakehouse, Warehouse, and modern analytics concepts
- Data security, access control, managed identities, and private connectivity concepts
- Monitoring, troubleshooting, optimization, and cost-awareness
- End-to-end project scenario: ingest, transform, store, and serve analytical data
Schedule
Next public session: June 10-13, 2026
Time: 09:00 AM - 05:00 PM Eastern Time
Format: Live online instructor-led training
Corporate and private group sessions can also be scheduled upon request.
Delivery and Materials
The course is delivered live online. Access details, meeting links, preparation instructions, and any required materials are sent by email after successful registration and payment.
Course content is provided for registered participants only. Unauthorized recording, redistribution, or sharing of training sessions or materials is not permitted.