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Microsoft Fabric Analytics Engineer
Course Description
Overview
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.
Objectives
Audience
Topics
- Introduction
- 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
- Knowledge check
- Introduction
- Connect to data with Spark
- Write data into a lakehouse
- Consider uses for ingested data
- Exercise - Ingest data with Spark and Microsoft Fabric notebooks
- Knowledge check
- Introduction
- Understand pipelines
- Use the Copy Data activity
- Use pipeline templates
- Run and monitor pipelines
- Exercise - Ingest data with a pipeline
- Knowledge check
- Introduction
- 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
- Knowledge check
- Introduction
- 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
- Knowledge check
- Introduction
- 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
- Knowledge check
- Introduction
- 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
- Knowledge check
- Introduction
- 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
- Knowledge check
- Introduction
- 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 Fabric
- Knowledge check
- Introduction
- 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
- Knowledge check
- Introduction
- Monitor capacity metrics
- Monitor current activity
- Monitor queries
- Exercise - Monitor a data warehouse in Microsoft Fabric
- Knowledge check
- Introduction
- Describe the significance of scalable models
- Implement Power BI data modeling best practices
- Configure large datasets
- Exercise: Create a star schema model
- Knowledge check
- Introduction
- Understand model relationships
- Set up relationships
- Use DAX relationship functions
- Understand relationship evaluation
- Exercise: Work with model relationships
- Knowledge check
- Introduction
- 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
- Knowledge check
- Introduction
- Restrict access to Power BI model data
- Restrict access to Power BI model objects
- Apply good modeling practices
- Exercise: Enforce model security
- Knowledge check
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