The course covers the methods and best practices for implementing data analytics solutions with Microsoft Fabric. It explores the use of the platform’s main components, such as lakehouses, data warehouses, notebooks, dataflows and pipelines, semantic models, and reports. Participants learn how to transform, model, and leverage data to create reusable analytical assets, while applying analytical best practices, including version control and enterprise‑grade deployment processes.
Objectives
At the end of this course, the participant will be able to design, deploy, and manage enterprise‑scale data analytics solutions with Microsoft Fabric, transforming data into reusable analytical assets and applying best practices in governance and deployment.
Is it for you ?
Data professionals who want to use Microsoft Fabric to create and deploy enterprise‑scale data analytics solutions.
Prerequisite
Participants must have obtained the PL‑300 certification or have equivalent experience using Power BI for data transformation, modeling, visualization, and sharing. They must also have experience in building and deploying enterprise‑level data analytics solutions.
Your benefits
Content
MODULE 1: Ingest data with Dataflows Gen2 in Microsoft Fabric
- Describe the capabilities of Dataflow (Gen2) in Microsoft Fabric
- Create Dataflow (Gen2) solutions to ingest and transform data
- Include a Dataflow (Gen2) in a pipeline
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MODULE 2: Ingest data with Spark and Microsoft Fabric notebooks
- Ingest external data into Fabric lakehouses using Spark
- Configure authentication and optimization for external sources
- Load data into the Lakehouse as files or Delta tables
MODULE 3: Use Data Factory pipelines in Microsoft Fabric
- Describe the capabilities of pipelines 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 the main 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 a medallion architecture
- Describe the principles of using medallion architecture in data management
- Apply the medallion architecture framework in the Microsoft Fabric environment
- Analyze data stored in the lakehouse using DirectLake in Power BI
- Describe best practices to ensure 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 appropriate scenarios for 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 warehouses in Microsoft Fabric
- Describe warehouses in Fabric
- Understand what a warehouse is compared to a data lakehouse
- Work with warehouses in Fabric
- Create and manage datasets in a warehouse
MODULE 9: Load data into a Microsoft Fabric warehouse
- Learn different strategies for loading 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 into a warehouse using T‑SQL
- Learn how to load and transform data with Dataflow (Gen2)
MODULE 10: Query a warehouse in Microsoft Fabric
- Use the SQL query editor to query a data warehouse
- Explore how the visual query editor works
- Learn how to connect to 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 the current activity of a data warehouse with dynamic management views
- Monitor query 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 relationships between Power BI models
- Understand how relationships between models work
- Configure 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: Strengthen Power BI model security
- Restrict access to Power BI model data with RLS
- Restrict access to Power BI model objects with OLS
- Apply development best practices to strengthen Power BI model security
💡 Informations utiles
Our training programs are delivered in private sessions tailored to your needs—on-site at your location, virtually, or in our training rooms. Dates, schedules, language, and format are all fully customizable to fit your team. Ready to get started? Fill out the request form below to plan your training or receive a personalized proposal—our team will be happy to connect with you shortly.
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