DP-600T00 Microsoft Fabric Analytics Engineer

Course Duration

4 Days

Audience

Employees of federal, state and local governments; and businesses working with the government.

Prerequisites

No prerequisites required.

Course Description

This course covers methods and practices for implementing and managing enterprise-scale data analytics solutions using Microsoft Fabric. Students will build on existing analytics experience and will learn how to use Microsoft Fabric components, including lakehouses, data warehouses, notebooks, dataflows, data pipelines, and semantic models, to create and deploy analytics assets.

Course Outline

• 1 – Introduction to end-to-end analytics using Microsoft Fabric
• 2 – Get started with lakehouses in Microsoft Fabric
• 3 – Use Apache Spark in Microsoft Fabric
• 4 – Work with Delta Lake tables in Microsoft Fabric
• 5 – Use Data Factory pipelines in Microsoft Fabric
• 6 – Ingest Data with Dataflows Gen2 in Microsoft Fabric
• 7 – Get started with data warehouses in Microsoft Fabric
• 8 – Get started with Real-Time Intelligence in Microsoft Fabric
• 9 – Get started with data science in Microsoft Fabric
• 10 – Get started with Data Activator in Microsoft Fabric
• 11 – Administer Microsoft Fabric
• 12 – Introduction to end-to-end analytics using Microsoft Fabric
• 13 – Get started with lakehouses in Microsoft Fabric
• 14 – Use Apache Spark in Microsoft Fabric
• 15 – Work with Delta Lake tables in Microsoft Fabric
• 16 – Ingest Data with Dataflows Gen2 in Microsoft Fabric
• 17 – Use Data Factory pipelines in Microsoft Fabric
• 18 – Organize a Fabric lakehouse using medallion architecture design
• 19 – Ingest Data with Dataflows Gen2 in Microsoft Fabric
• 20 – Ingest data with Spark and Microsoft Fabric notebooks
• 21 – Use Data Factory pipelines in Microsoft Fabric
• 22 – Get started with data warehouses in Microsoft Fabric
• 23 – Load data into a Microsoft Fabric data warehouse
• 24 – Query a data warehouse in Microsoft Fabric
• 25 – Monitor a Microsoft Fabric data warehouse
• 26 – Understand scalability in Power BI
• 27 – Create Power BI model relationships
• 28 – Use tools to optimize Power BI performance
• 29 – Enforce Power BI model security