DP-600T00 - Microsoft Fabric Analytics Engineer
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. 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.
Duration: 4 days
Who should attend
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.
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
Ingest data with Spark and Microsoft Fabric notebooks
Connect to data with Spark Write data into a lakehouse Consider uses for ingested data
Use Data Factory pipelines in Microsoft Fabric
Understand pipelines Use the Copy Data activity Use pipeline templates Run and monitor pipelines
Get started with lakehouses in Microsoft Fabric
Explore the Microsoft Fabric Lakehouse Work with Microsoft Fabric Lakehouses Explore and transform data in a lakehouse
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
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
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
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
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
Query a data warehouse in Microsoft Fabric
Use the SQL query editor Explore the visual query editor Use client tools to query a warehouse
Monitor a Microsoft Fabric data warehouse
Monitor capacity metrics Monitor current activity Monitor queries
Understand scalability in Power BI
Describe the significance of scalable models Implement Power BI data modelling best practices Configure large datasets
Create Power BI model relationships
Understand model relationships Set up relationships Use DAX relationship functions Understand relationship evaluation
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
Enforce Power BI model security
Restrict access to Power BI model data Restrict access to Power BI model objects Apply good modeling practices
Schedule
| Name | Date | Location | |
|---|---|---|---|
| DP-600T00 - Microsoft Fabric Analytics Engineer | 2024-09-17 | Online |
Azure SQL Azure Apache Spark Lakehouse Fabric Microsoft Fabric Delta Lake Dataflows Gen2 Data Factory Power BI