In this section, we explore Microsoft Fabric workloads, including Data Factory, Data Engineering, and Real-Time Analytics, to build end-to-end analytics systems with practical applications.

In this section, we explore building an end-to-end analytics system using a lakehouse architecture with Medallion layers, Delta Lake, and OneLake for unified data storage and analysis.

In this section, we explore building an end-to-end analytics system using a data warehouse, focusing on data ingestion, T-SQL transformation, and ETL orchestration with Data Factory for practical data processing and reporting.

In this section, we explore real-time analytics systems, focusing on data pipelines, Kusto Query Language, and Power BI reporting for scalable data solutions.

In this section, we explore building an end-to-end analytics system using AI and ML, focusing on data ingestion, model design with LightGBM and FLAML, and operationalizing models in Microsoft Fabric.

This section covers monitoring Data Factory, Spark, and real-time analytics in Fabric for improved performance and reliability.

In this section, we explore enabling Microsoft Fabric in a tenant, managing Spark job configurations, and associating capacities with workspaces for efficient resource allocation and performance optimization.

In this section, we examine Microsoft Fabric's security and governance frameworks, focusing on platform protection, workspace permissions, and compliance strategies to ensure data integrity and regulatory adherence.

This section covers CI/CD pipelines, Git integration, and managing database code in Fabric data warehouses.

This section explores Copilot's AI capabilities in Fabric, focusing on data engineering, Power BI, and narrative creation.