Microsoft Fabric Training in Varna

Microsoft Fabric serves as a comprehensive analytics platform that consolidates data engineering, integration, warehousing, science, real-time analytics, and business intelligence into a single cohesive environment.

This instructor-led training, available both online and on-site, is designed for intermediate data professionals aiming to utilize Microsoft Fabric for constructing contemporary data solutions equipped with integrated analytics, governance, and visualization capabilities.

Upon completing this course, participants will be capable of:

• Gaining insight into Microsoft Fabric's components and their integration within the Microsoft ecosystem.
• Establishing and overseeing Fabric Workspaces with appropriate access controls.
• Developing and maintaining Lakehouses to centralize and analyze data effectively.
• Employing notebooks for exploratory analysis and transformations utilizing Python and SQL.
• Designing and automating data flows through visual pipelines.
• Modeling and managing data across the Data Warehouse and Semantic Model layers.
• Creating and publishing interactive dashboards using Power BI within Fabric.

Course Format

• Engaging lectures and discussions.
• Extensive exercises and practical application.
• Practical implementation in a live-lab setting.

Customization Options

• For customized training requests, please get in touch to arrange your specific needs.

This instructor-led, live training session in Varna (offered online or onsite) is designed for intermediate-level data professionals seeking to implement a lakehouse solution using Microsoft Fabric.

Upon completion of this training, participants will be able to:

• Understand the architecture and key components of Microsoft Fabric.
• Configure and manage a Lakehouse environment using Microsoft Fabric.
• Ingest and transform data using Apache Spark.
• Utilize Delta Lake tables for versioning and managing data.
• Implement data ingestion pipelines with Dataflows Gen2.
• Orchestrate data pipelines using Data Factory.