Microsoft Fabric Analytics Engineer (DP-600)

Er is geen verplichte voorkennis benodigd, wel is het aan te raden om voor deze training de PL-300 te hebben gedaan of vergelijkbare kennis in Power BI te hebben.

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.

Module 1: Ingest Data with Dataflows Gen2 in Microsoft Fabric

• Describe Dataflow (Gen2) capabilities in Microsoft Fabric
• Create Dataflow (Gen2) solutions to ingest and transform data
• Include a Dataflow (Gen2) in a pipeline

Module 2: Ingest data with Spark and Microsoft Fabric notebooks

• Ingest external data to Fabric lakehouses using Spark
• Configure external source authentication and optimization
• Load data into lakehouse as files or as Delta tables

Module 3: Use Data Factory pipelines in Microsoft Fabric

• Describe pipeline capabilities in Microsoft Fabric
• Use the Copy Data activity in a pipeline
• Create pipelines based on predefined templates
• Run and monitor pipelines

Module 4: Get started with lakehouses in Microsoft Fabric

• Describe core features and capabilities of lakehouses in Microsoft Fabric
• Create a lakehouse
• Ingest data into files and tables in a lakehouse
• Query lakehouse tables with SQL

Module 5: Organize a Fabric lakehouse using medallion architecture design

• Describe the principles of using the medallion architecture in data management.
• Apply the medallion architecture framework within the Microsoft Fabric environment.
• Analyze data stored in the lakehouse using DirectLake in Power BI.
• Describe best practices for ensuring the security and governance of data stored in the medallion architecture.

Module 6: Use Apache Spark in Microsoft Fabric

• Configure Spark in a Microsoft Fabric workspace
• Identify suitable scenarios for Spark notebooks and Spark jobs
• Use Spark dataframes to analyze and transform data
• Use Spark SQL to query data in tables and views
• Visualize data in a Spark notebook

Module 7: Work with Delta Lake tables in Microsoft Fabric

• Understand Delta Lake and delta tables in Microsoft Fabric
• Create and manage delta tables using Spark
• Use Spark to query and transform data in delta tables
• Use delta tables with Spark structured streaming

Module 8: Get started with data warehouses in Microsoft Fabric

• Describe data warehouses in Fabric
• Understand a data warehouse vs a data Lakehouse
• Work with data warehouses in Fabric
• Create and manage datasets within a data warehouse

Module 9: Load data into a Microsoft Fabric data warehouse

• Learn different strategies to load data into a data warehouse in Microsoft Fabric.
• Learn how to build a data pipeline to load a warehouse in Microsoft Fabric.
• Learn how to load data in a warehouse using T-SQL.
• Learn how to load and transform data with dataflow (Gen 2).

Module 10: Query a data warehouse in Microsoft Fabric

• Use SQL query editor to query a data warehouse.
• Explore how visual query editor works.
• Learn how to connect and query a data warehouse using SQL Server Management Studio.

Module 11: Monitor a Microsoft Fabric data warehouse

• Monitor capacity unit usage with the Microsoft Fabric Capacity Metrics app.
• Monitor current activity in the data warehouse with dynamic management views.
• Monitor querying trends with query insights views.

Module 12: Understand scalability in Power BI

• Describe the importance of building scalable data models
• Implement Power BI data modeling best practices
• Use the Power BI large dataset storage format

Module 13: Create Power BI model relationships

• Understand how model relationship work.
• Set up relationships.
• Use DAX relationship functions.
• Understand relationship evaluation.

Module 14: Use tools to optimize Power BI performance

• Optimize queries using performance analyzer.
• Troubleshoot DAX performance using DAX Studio.
• Optimize a data model using Tabular Editor.

Module 15: Enforce Power BI model security

• Restrict access to Power BI model data with RLS.
• Restrict access to Power BI model objects with OLS.
• Apply good development practices to enforce Power BI model security.