Microsoft Fabric Data Engineer

Microsoft via Coursera Professional Certificate

Go to class Write review

Details

Go to class

Provider

Coursera Professional Certificate
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Sessions

Self-Paced
Level

Intermediate

Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

Unlock All Certificates

The Microsoft Fabric Data Engineer Professional Certificate prepares you for data engineering roles, where you will build and manage data workflows in modern data environments. Organizations rely on data engineers to move data across systems, prepare it for analysis, and ensure data platforms operate reliably and securely. Designed for aspiring data engineers and data professionals, this program builds practical capability to understand platform architecture, ingest and transform data, and manage workflows across a unified data platform. You will learn to ingest data from multiple sources, structure and transform it for analytical use, and work with data across Fabric components such as Lakehouses and pipelines. You will also develop the ability to prepare analytics-ready datasets, orchestrate workflows, automate data movement, and monitor pipeline execution. As the program progresses, you will build operational capability by troubleshooting pipeline issues, managing platform performance, applying governance practices, and identifying improvements based on system usage and reliability. Through applied scenarios and practical activities, you will build and manage data workflows using Microsoft Fabric tools such as OneLake, Dataflows Gen2, pipelines, and Spark-based processing. By completing this program, you will strengthen your readiness for data engineering roles, with the ability to build, manage, and evaluate data workflows that support analytics and reporting.

Syllabus

Course 1: Fabric Foundations and Environment Management
Course 2: Preparing Data for Analytics in Microsoft Fabric
Course 3: Orchestrating Data Pipelines in Microsoft Fabric
Course 4: Operating and Governing Data Platforms in Microsoft Fabric

Courses

0 reviews

View details

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.
0 reviews

View details

This course focuses on the operational management, performance optimization, and governance practices required to maintain production data engineering systems within Microsoft Fabric environments. After pipelines, streaming workflows, and analytics datasets have been implemented, data engineers must ensure that these systems operate efficiently, securely, and reliably within enterprise environments. This requires monitoring system performance, managing platform capacity, implementing security controls, and maintaining data lifecycle policies. Learners explore how engineers optimize data pipeline performance, manage platform resource usage, and implement governance mechanisms that protect data assets while enabling analytics teams to work effectively. The course also examines how enterprise data platforms enforce security and compliance controls through role-based access management, data classification, and governance policies. Finally, learners explore how data lifecycle management practices ensure that datasets remain reliable, secure, and properly maintained throughout their operational lifespan. By the end of the course, learners understand how engineers manage performance, security, governance, and lifecycle policies within Microsoft Fabric environments.
0 reviews

View details

This course focuses on the orchestration, automation, and monitoring of data engineering workflows within Microsoft Fabric. After datasets have been ingested, transformed, and structured for analytics, data engineers must coordinate how those processes run reliably across enterprise environments. Learners examine how Microsoft Fabric pipelines orchestrate multi-step data workflows, automate scheduled processing tasks, and coordinate dependencies between ingestion, transformation, and analytics operations. The course also introduces real-time data engineering concepts through streaming architectures, demonstrating how continuous data sources such as telemetry or operational events can be integrated into Fabric environments. Finally, learners explore monitoring and troubleshooting practices used to maintain reliable data pipelines. By understanding how engineers observe pipeline execution, identify failures, and diagnose performance issues, learners develop the operational skills required to maintain production data engineering systems. By the end of the course, learners understand how Fabric pipelines coordinate complex workflows, how streaming architectures support real-time data processing, and how engineers monitor and troubleshoot data engineering systems in production environments.
0 reviews

View details

This course focuses on preparing integrated datasets for analytics, reporting, and business intelligence workloads within Microsoft Fabric. Learners explore how data engineers structure datasets so they can be efficiently queried, modeled, and used by analytics tools such as Power BI and SQL endpoints. The course introduces key concepts that transform integrated datasets into analytics-ready data assets, including dataset structure, modeling considerations, and query workflows. Learners examine how Lakehouse tables and Fabric Warehouses support analytical workloads and how structured datasets enable reliable reporting and decision-making. Through practical scenarios and guided demonstrations, learners develop an understanding of how data engineers prepare datasets for analytical consumption, ensuring that datasets are reliable, accessible, and optimized for downstream analytics tools. The course concludes with a portfolio project in which learners prepare and validate an analytics-ready dataset within a Fabric environment.