• Master Microsoft Fabric for data ingestion, transformation, and analysis
• Learn to use key Fabric components such as Data Factory, Lakehouses, and Delta Lake tables for efficient ETL tasks
• Apply ETL principles in real-world scenarios
• Implement security measures, monitor pipelines, and optimize performance

• Microsoft Fabric overview : introduction to the fundamental principles of ETL and key components such as Data Factory, Lakehouses, and Delta Lake
• Data Pipelines with Data Factory : create and ma-age data pipelines, ingest, transform, and auto-mate real-time data processes using Dataflows Gen2
• Lakehouses and Delta Lake Tables : manage and transform streaming data efficiently using Delta tables and Spark
• Security and Optimization : implement security measures, monitor pipelines, and optimize perfor-mance using techniques like query folding

This training is designed to be highly practical, ensuring that every theoretical concept is immediately reinforced through hands-on exercises. The course follows a continuous scenario-based approach, where participants progressively build on previous learnings, reinforcing key concepts through repetition and practical application. As the training progresses, participants are encouraged to apply previously covered concepts autonomously, fostering full independence by the end of the course. Additionally, theoretical elements are regularly tested and discussed throughout the sessions to ensure a deep and structured understanding. This methodology guarantees that participants not only grasp theoretical principles but also gain real-world proficiency in applying them.

Our instructors have extensive hands-on experience with Microsoft Fabric and ETL processes, ensuring participants receive practical and applicable learning. Each session is designed to help participants master the complexities of data ingestion, transformation, and real-time analysis using the core features of Microsoft Fabric. Full access to all course materials will be provided to ensure a comprehensive learning experience.