About this course
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 lake houses, data warehouses, notebooks, dataflows, data pipelines, and semantic models, to create and deploy analytics assets.
This course is best suited for those who have the PL-300 certification or similar expertise in using Power BI for data transformation, modeling, visualization, and sharing. Also, learners should have prior experience in building and deploying data analytics solutions at the enterprise level.
Audience Profile
The primary audience for this course is data professionals with experience in data modeling, extraction, and analytics. DP-600 is designed for professionals who want to use Microsoft Fabric to create and deploy enterprise-scale data analytics solutions.
Course Outline
Module 1: Ingest Data with Dataflows Gen2 in Microsoft Fabric.
• Understand Dataflows Gen2 in Microsoft Fabric.
• Explore Dataflows Gen2 in Microsoft Fabric.
• Integrate Dataflows Gen2 and Pipelines in Microsoft Fabric.
• Exercise – Create and use a Dataflow Gen2 in Microsoft Fabric.
Module 2: Ingest data with Spark and Microsoft Fabric notebooks.
• Connect to data with Spark.
• Write data into a lakehouse.
• Consider uses for ingested data.
• Exercise – Ingest data with Spark and Microsoft Fabric notebooks.
Module 3: Use Data Factory pipelines in Microsoft Fabric.
• Understand pipelines.
• Use the Copy Data activity.
• Use pipeline templates.
• Run and monitor pipelines.
• Exercise – Ingest data with a pipeline.
Module 4: Get started with lakehouses in Microsoft Fabric.
• Explore the Microsoft Fabric Lakehouse.
• Work with Microsoft Fabric Lakehouses.
• Explore and transform data in a lakehouse.
• Exercise – Create and ingest data with a Microsoft Fabric Lakehouse.
Module 5: Organize a Fabric lakehouse using medallion architecture design.
• Describe medallion architecture.
• Implement a medallion architecture in Fabric.
• Query and report on data in your Fabric lakehouse.
• Considerations for managing your lakehouse.
• Exercise – Organize your Fabric lakehouse using a medallion architecture.
Module 6: Use Apache Spark in Microsoft Fabric.
• Prepare to use Apache Spark.
• Run Spark code.
• Work with data in a Spark dataframe.
• Work with data using Spark SQL.
• Visualize data in a Spark notebook.
• Exercise – Analyze data with Apache Spark.
Module 7: Work with Delta Lake tables in Microsoft Fabric.
• Understand Delta Lake.
• Create delta tables.
• Work with delta tables in Spark.
• Use delta tables with streaming data.
• Exercise – Use delta tables in Apache Spark.
Module 8: Get started with data warehouses in Microsoft Fabric.
• Understand data warehouse fundamentals.
• Understand data warehouses in Fabric.
• Query and transform data.
• Prepare data for analysis and reporting.
• Secure and monitor your data warehouse.
• Exercise – Analyze data in a data warehouse.
Module 9: Load data into a Microsoft Fabric data warehouse.
• Explore data load strategies.
• Use data pipelines to load a warehouse.
• Load data using T-SQL.
• Load and transform data with Dataflow Gen2.
• Exercise: Load data into a warehouse in Microsoft Fbric.
Module 10: Query a data warehouse in Microsoft Fabric.
• Use the SQL query editor.
• Explore the visual query editor.
• Use client tools to query a warehouse.
• Exercise: Query a data warehouse in Microsoft Fabric.
Module 11: Monitor a Microsoft Fabric data warehouse.
• Monitor capacity metrics.
• Monitor current activity.
• Monitor queries.
• Exercise – Monitor a data warehouse in Microsoft Fabric.
Module 12: Understand scalability in Power BI.
• Describe the significance of scalable models.
• Implement Power BI data modeling best practices.
• Configure large datasets.
• Exercise: Create a star schema model.
Module 13: Create Power BI model relationships.
• Understand model relationships.
• Set up relationships.
• Use DAX relationship functions.
• Understand relationship evaluation.
• Exercise: Work with model relationships.
Module 14: Use tools to optimize Power BI performance.
• Use Performance analyzer.
• Troubleshoot DAX performance by using DAX Studio.
• Optimize a data model by using Best Practice Analyzer.
• Exercise: Use tools to optimize Power BI performance.
Module 15: Enforce Power BI model security.
• Restrict access to Power BI model data.
• Restrict access to Power BI model objects.
• Apply good modeling practices.
• Exercise: Enforce model security.
