Microsoft Fabric: Implement and Manage Analytics Solutions

This comprehensive certification course is designed for data professionals aiming to master end-to-end analytics design, implementation, and management using Microsoft Fabric. Dive deep into Microsoft’s unified data platform and learn how to configure, secure, and orchestrate data solutions across Fabric’s integrated services, covering data engineering, data science, and business intelligence workloads. The course delivers approximately 6–7 hours of video lectures, combining conceptual understanding with hands-on demonstrations mapped to the official Microsoft Certified: Fabric Data Engineer Associate (Exam DP-700) objectives. Each module includes quizzes and in-video assessments to reinforce key learning outcomes and prepare you for real-world analytics challenges. Enroll in “Microsoft Fabric: Implement and Manage Analytics Solutions” to gain the expertise required to build, secure, and scale modern analytics environments in Microsoft Fabric. Course Modules: Implement and Manage an Analytics Solutions Ingest and transform data Monitor and optimize an analytics solution By end of this course, you will be able to learn about - You’ll explore Microsoft Fabric’s core architecture, ecosystem, and governance features, - You will learn how to administer environments, secure data access, and orchestrate analytics workflows through real-world examples and guided demos. - From CI/CD implementation and workspace management to security, compliance, and data pipeline orchestration, this course equips you with the expertise to operationalize analytics at scale in enterprise settings. As a candidate for this exam, you should have subject matter expertise with data loading patterns, data architectures, and orchestration processes. You work closely with analytics engineers, architects, analysts, and administrators to design and deploy data engineering solutions for analytics. You should be skilled at manipulating and transforming data by using Structured Query Language (SQL), PySpark, and Kusto Query Language (KQL).