This module introduces the core ingestion workflows used by data engineers to bring external datasets into Microsoft Fabric environments. You'll examine how structured and semi-structured data sources are connected to Fabric Lakehouses and how ingestion pipelines ensure datasets are consistently available for analytics and reporting. The module focuses on the ingestion stage of the data engineering lifecycle, where raw data from files, APIs, and operational systems is imported into Fabric environments using Dataflow Gen2. You'll explore how ingestion workflows transform disconnected data sources into unified datasets that support downstream transformation and analytics processes. Rather than attempting complex transformation pipelines, the module emphasizes reliable ingestion fundamentals, including connecting to data sources, validating schemas, and writing datasets to a Lakehouse using Delta format. By the end of the module, you'll understand how Fabric ingestion workflows move data from external sources into the Lakehouse environment and how these ingestion steps support broader data engineering pipelines.

This module introduces data transformation workflows within Microsoft Fabric and explains how data engineers convert raw ingested datasets into structured, analytics-ready tables. You'll explore how Dataflow Gen2 supports lightweight transformation tasks such as filtering records, renaming fields, standardizing data types, and shaping datasets for downstream analytics workloads. Rather than treating ingestion and transformation as separate technical disciplines, the module demonstrates how transformation steps are often performed immediately after ingestion to prepare datasets for consistent reporting and analysis. By examining practical transformation scenarios, you'll develop an understanding of how engineers ensure that ingested datasets become reliable, structured resources that analytics teams and business intelligence tools can consume.

This module introduces data integration workflows within Microsoft Fabric and explains how data engineers combine datasets originating from multiple sources into unified, analytics-ready tables. You will explore how separate datasets often represent different aspects of a business process and must be combined to provide meaningful analytical insight. The module examines how integration operations such as joins, schema alignment, and record validation allow engineers to merge datasets while preserving consistency and accuracy. Using Dataflow Gen2, you'll observe how datasets imported from different files or systems can be connected and integrated within a single workflow before being written to a Fabric Lakehouse. By understanding how integration workflows combine multiple datasets into coherent structures, you'll develop the conceptual and technical foundation required to prepare complex datasets used in reporting, analytics, and machine learning pipelines.