DP-500T00: Designing and Implementing Enterprise-Scale: 4 Days Workshop


Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI course covers methods and practices for performing advanced data analytics at scale.

Students will learn to implement and manage a data analytics environment, query and transform data, implement and manage data models, and explore and visualize data. In this DP-500 training, students will use Microsoft Purview, Azure Synapse Analytics, and Power BI to build analytics solutions.

Target Audience

• Candidates for this course should have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions. Specifically, candidates should have advanced Power BI skills, including managing data repositories and data processing in the cloud and on-premises, along with using Power Query and Data Analysis Expressions (DAX).
• They should also be proficient in consuming data from Azure Synapse Analytics and should have experience querying relational databases, analyzing data by using Transact-SQL (T-SQL), and visualizing data.



• A foundational knowledge of core data concepts and how they’re implemented using Azure data services.
• Experience designing and building scalable data models, cleaning and transforming data, and enabling advanced analytic capabilities that provide meaningful business value using Microsoft Power BI.


Day 1

• Introduction to data analytics on Azure

	1. Explore Azure data services for modern analytics
	2. Understand concepts of data analytics
	3. Explore data analytics at scale

• Govern data across an enterprise

	1. Introduction to Microsoft Purview
	2. Discover trusted data using Microsoft Purview
	3. Catalog data artifacts by using Microsoft Purview
	4. Manage Power BI artifacts by using Microsoft Purview

• Model, query, and explore data in Azure Synapse

	1. Introduction to Azure Synapse Analytics
	2. Use Azure Synapse serverless SQL pool to query files in a data lake
	3. Analyze data with Apache Spark in Azure Synapse Analytics
	4. Analyze data in a relational data warehouse
	5. Lab: Query data in Azure
	6. Lab: Explore data in Spark notebooks
	7. Lab: Create a star schema model

Day 2

• Prepare data for tabular models in Power BI

	1. Choose a Power BI model framework
	2. Understand scalability in Power BI
	3. Optimize Power Query for scalable solutions
	4. Create and manage scalable Power BI dataflows
	5. Lab: Create a dataflow
• Design and build scalable tabular models

	1. Create Power BI model relationships
	2. Enforce model security
	3. Implement DirectQuery
	4. Create calculation groups
	5. Use tools to optimize Power BI performance
	6. Lab: Create model relationships
	7. Lab: Enforce model security
	8. Lab: Design and build tabular models
	9. Lab: Create calculation groups
	10. Lab: Use tools to optimize Power BI performance

Day 3

• Implement advanced data visualization techniques by using Power BI

	1. Understand advanced data visualization concepts
	2. Customize core data models
	3. Monitor data in real-time with Power BI
	4. Create and distribute paginated reports in Power BI report builder
	5. Lab: Monitor data in real-time with Power BI
	6. Lab: Create and distribute paginated reports in Power BI Report Builder
• Implement and manage an analytics environment

	1. Provide governance in a Power BI environment
	2. Facilitate collaboration and sharing in Power BI
	3. Monitor and audit usage
	4. Provision Premium capacity in Power BI
	5. Establish a data access infrastructure in Power BI
	6. Broaden the reach of Power BI
	7. Automate Power BI administration
	8. Build reports using Power BI within Azure Synapse Analytics

Day 4

• Manage the analytics development lifecycle

	1. Design a Power BI application lifecycle management strategy
	2. Create and manage a Power BI deployment pipeline
	3. Create and manage Power BI assets
	4. Lab: Create reusable Power BI assets
• Integrate an analytics platform into an existing IT infrastructure

	1. Recommend and configure a Power BI tenant or workspace
	2. Identify requirements for a solution, including features, performance, and licensing strategy
	3. Integrate an existing Power BI workspace into Azure Synapse Analytics