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Fabric Foundations and Environment Management

Microsoft via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 2 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Part of

Microsoft Fabric Data Engineer

Overview

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This course introduces the core concepts, architecture, and workflows that define modern data engineering using Microsoft Fabric. Learners develop a working understanding of how data engineers ingest, organize, transform, and prepare data so it can support analytics, reporting, and AI workloads. The course focuses on the early stages of the data engineering lifecycle: understanding Fabric architecture, working with Lakehouses, ingesting data from multiple sources, and preparing structured datasets that support downstream analytics. Rather than treating Fabric tools as isolated features, the course emphasizes how these tools work together within a unified data platform. Learners explore how data engineers move data from raw ingestion to structured, analytics-ready datasets while maintaining reliability, scalability, and data quality. By the end of the course, learners understand how Fabric’s Lakehouse architecture, Dataflows Gen2, and integration workflows support end-to-end data engineering pipelines.

Syllabus

Introduction to the Microsoft Fabric data platform

This module introduces Microsoft Fabric as a unified analytics platform and explains how its architecture supports modern data engineering workflows. You'll explore how Fabric integrates storage, ingestion, transformation, and analytics capabilities into a single environment built around the Lakehouse model. The module focuses on the foundational role data engineers play in preparing reliable datasets for analytics and AI workloads. You'll examine how raw data moves through Fabric systems, how Lakehouses organize data using the Delta format, and how ingestion pipelines ensure data is consistently available for downstream processing. By understanding Fabric’s architecture and the responsibilities of data engineers within this environment, you will establish the conceptual foundation required for building ingestion pipelines and data integration workflows in later modules.

Ingesting data into Microsoft Fabric

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.

Transforming Data with Dataflow Gen2

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.

Integrating Multiple Data Sources In Microsoft Fabric

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.