This module introduces the fundamentals of data modeling for analytics within Microsoft Fabric environments. You will examine how structured datasets are organized so that analytics tools and reporting systems can query data efficiently and consistently. The module focuses on the principles used to structure analytical datasets, including the use of fact and dimension tables, relationships between entities, and the organization of datasets into schemas that support analytical queries. Rather than focusing on complex database theory, the module emphasizes practical modeling decisions that data engineers make when preparing datasets for analytics workloads. You will explore how structured analytical models enable reporting tools such as Power BI to produce accurate insights and reliable dashboards. By the end of the module, you will understand how data engineers design dataset structures that support efficient analytical queries and enable downstream business intelligence workflows.

This module introduces how structured datasets stored within Microsoft Fabric Lakehouses and Warehouses are queried to support analytics workflows. You will explore how analytical queries allow engineers and analysts to retrieve, validate, and interpret structured data prepared for reporting and business intelligence. The module focuses on the role of SQL-based queries within analytics environments. You will examine how queries allow engineers to inspect datasets, confirm that analytical structures are correct, and retrieve insights from structured tables. Rather than focusing on advanced SQL techniques, the module emphasizes how queries are used within the data engineering workflow to validate datasets and support downstream reporting tools such as Power BI. By the end of the module, you will understand how querying structured datasets enables engineers and analysts to interact with analytics-ready data stored within Microsoft Fabric.

This module focuses on preparing structured datasets so they can be reliably used by reporting and analytics tools. You will examine how data engineers ensure that analytics-ready datasets support consistent reporting results and can be accessed efficiently by analytical applications. The module explores how structured tables, validated schemas, and clearly defined relationships allow reporting systems to query datasets and produce accurate analytical outputs. You will observe how engineers confirm that datasets support analytical queries and reporting tools before they are made available to analysts and business intelligence systems. By the end of the module, you will understand how engineers verify that datasets are ready for reporting workflows and how properly prepared datasets enable reliable dashboards, reports, and analytical insights.

This module introduces how generative AI tools can assist data engineers during data transformation workflows. Rather than replacing engineering work, AI systems can help engineers draft SQL queries, propose transformation logic, and explain dataset structures during development. You will explore how AI-generated suggestions can accelerate common data preparation tasks such as filtering records, restructuring fields, and generating aggregation queries. The module emphasizes the importance of validating AI-generated outputs to ensure that transformation logic remains correct, efficient, and aligned with data engineering standards. By the end of the module, you will understand how AI assistants can support transformation workflows while maintaining the engineer’s responsibility for verifying correctness and reliability.