DP-600: Implement analytics solutions with Microsoft Fabric

Course Description: DP-600: Implement Analytics Solutions with Microsoft Fabric

In this course, you will learn how to implement end-to-end analytics solutions with Microsoft Fabric . You will learn to:

  • Plan, implement, and manage a data analytics solution
  • Prepare and serve data
  • Deploy and manage semantic models
  • Explore and analyze data

Public Profile

This DP-600 course is primarily intended for data professionals who are familiar with data modeling, extraction, and analysis.

This course is designed for data professionals interested in using the Microsoft Fabric platform to build and deploy end-to-end analytics solutions at enterprise scale using Fabric.

Items in this collection

  • Ingest data with Microsoft Fabric
  • Deploy a Lakehouse instance with Microsoft Fabric
  • Deploy a data warehouse with Microsoft Fabric
  • Work with semantic models in Microsoft Fabric
  • Design and creation of tabular models
  • Analytics Development Lifecycle Management

    Course outline

    Exploring end-to-end analytics with Microsoft Fabric

    • Describes end-to-end analytics in Microsoft Fabric
    • Understand data teams and roles that use Fabric
    • Describes how to enable and use Fabric

    Microsoft Fabric Administration

    • Describes Fabric admin tasks
    • Navigate the admin center
    • Manage user access

    Ingesting data with Gen2 data flows in Microsoft Fabric

    Ingesting data with Spark and Microsoft Fabric notebooks

    Using Data Factory pipelines in Microsoft Fabric

    Introduction to Lake Stores in Microsoft Fabric

    Using Apache Spark on Microsoft Fabric

    Using Delta Lake tables in Microsoft Fabric

    Protect your Fabric Lake warehouse

    Introduction to data stores in Microsoft Fabric

    Loading data to a Microsoft Fabric data store

    Querying a data store in Microsoft Fabric

    Monitoring a Microsoft Fabric Data Store

    Protect your Fabric data storage

    Scalability in Power BI

    • Description of the importance of creating scalable semantic models
    • Implement Power BI data modeling best practices
    • Using the Power BI large data set storage format

    Create model relationships

    • Create relationships.
    • Use DAX relationship functions.
    • Understand relationship evaluation.

    Using tools to optimize Power BI performance

    • Optimize solutions using aggregations, dual storage mode, and hybrid tables.
    • Optimize queries with the performance analyzer.
    • Troubleshoot DAX performance issues using DAX Studio.
    • Optimize a data model using Tabular Editor

    Apply semantic 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 security for Power BI models.

    Previous requirements


    • Course: English / Spanish

    • Labs: English

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