Transform your enterprise data analytics expertise with the Microsoft Fabric Analytics Engineer course, designed for certified professionals seeking to leverage cutting-edge tools like lakehouses, notebooks, and data pipelines for scalable data solutions.
EnrollImplement and manage enterprise-scale data analytics solutions using Microsoft Fabric.
Utilize Microsoft Fabric components such as lakehouses, data warehouses, and semantic models to create and deploy analytics assets.
Harness data ingestion and transformation with Apache Spark and Power BI.
Optimize Power BI performance and enforce robust security models.
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.
Explore end-to-end analytics with Microsoft Fabric
Data teams and Microsoft Fabric
Enable and use Microsoft Fabric
Understand the Fabric Architecture
Understand the Fabric administrator role
Manage Fabric security
Govern data in Fabric
Understand Dataflows Gen2 in Microsoft Fabric
Explore Dataflows Gen2 in Microsoft Fabric
Integrate Dataflows Gen2 and Pipelines in Microsoft Fabric
Connect to data with Spark
Write data into a lakehouse
Consider uses for ingested data
Understand pipelines
Use the Copy Data activity
Use pipeline templates
Run and monitor pipelines
Explore the Microsoft Fabric lakehouse
Work with Microsoft Fabric lakehouses
Explore and transform data in a lakehouse
Describe medallion architecture
Implement a medallion architecture in Fabric
Query and report on data in your Fabric lakehouse
Considerations for managing your lakehouse
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
Understand Delta Lake
Create delta tables
Work with delta tables in Spark
Use delta tables with streaming data
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
Explore data load strategies
Use data pipelines to load a warehouse
Load data using T-SQL
Load and transform data with Dataflow Gen2
Use the SQL query editor
Explore the visual query editor
Use client tools to query a warehouse
Monitor capacity metrics
Monitor current activity
Monitor queries
Describe the significance of scalable models
Implement Power BI data modeling best practices
Configure large datasets
Understand model relationships
Set up relationships
Use DAX relationship functions
Understand relationship evaluation
Use Performance analyzer
Optimize a data model by using Best Practice Analyzer
Troubleshoot DAX performance by using DAX Studio
Restrict access to Power BI model data
Restrict access to Power BI model objects
Apply good modeling practices
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Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier
Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier