Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI

Before attending this course, it is recommended that students have: 

• A foundational knowledge of core data concepts and how they’re implemented using Azure data services. For more information see DP-900 Azure Data Fundamentals.
• 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. We recommend first following the PL-300 training if this is new to you.

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.

Module 1: Introduction to data analytics on AzureThis module explores key concepts of data analytics, including types of analytics, data, and storage. Students will explore the analytics process and tools used to discover insights and learn about the responsibilities of an enterprise data analyst and what tools are available to build scalable solutions.Lessons

• Explore Azure data services for modern analytics
• Understand concepts of data analytics
• Explore data analytics at scale

After completing this module, students will be able to:

• Describe types of data analytics
• Understand the data analytics process
• Define data job roles in analytics
• Understand tools for scaling analytics solutions

Module 2: Govern data across an enterpriseThis module explores the role of an enterprise data analyst in organizational data governance. Students will explore the use of Microsoft Purview to register and catalog data assets, to discover trusted assets for reporting, and to scan a Power BI environment.Lessons

• Introduction to Microsoft Purview
• Discover trusted data using Microsoft Purview
• Catalog data artifacts by using Microsoft Purview
• Manage Power BI artifacts by using Microsoft Purview

After completing this module, students will be able to:

• Browse, search, and manage data catalog assets.
• Use data catalog assets with Power BI.
• Use Microsoft Purview in Azure Synapse Studio.
• Register and scan a Power BI environment using Microsoft Purview.

Module 3: Model, query, and explore data in Azure SynapseThis module explores the use of Azure Synapse Analytics for exploratory data analysis. Students will explore the capabilities of Azure Synapse Analytics including the basics of data warehouse design, querying data using T-SQL, and exploring data using Spark notebooks.Lessons

• Introduction to Azure Synapse Analytics
• Implement star schema design and query relational data in Azure
• Analyze data with a serverless SQL pool in Azure Synapse Analytics
• Optimize data warehouse query design
• Analyze data with a Spark Pool in Azure Synapse Analytics

Lab : Query data in AzureLab : Explore data in Spark notebooksLab : Create a star schema modelAfter completing this module, students will be able to:

• Understand when to use Azure Synapse Analytics in reporting solutions.
• Query data with SQL.
• Query data with Spark.

Module 4: Prepare data for tabular models in Power BIThis module explores the fundamental elements of preparing data for scalable analytics solutions using Power BI. Students will explore model frameworks, considerations for building data models that will scale, Power Query optimization techniques, and the implementation of Power BI dataflows.Lessons

• Choose a Power BI model framework
• Understand scalability in Power BI
• Optimize Power Query for scalable solutions
• Create and manage scalable Power BI dataflows

Lab : Create a dataflowAfter completing this module, students will be able to:

• Choose an appropriate Power BI model framework.

Module 5: Design and build scalable tabular modelsThis module explores the critical underlying aspects of tabular modeling for building Power BI models that can scale. Students will learn about model relationships and model security, working with direct query, and using calculation groups.Lessons

• Create Power BI model relationships
• Enforce model security
• Implement DirectQuery
• Create calculation groups

Lab : Create model relationshipsLab : Enforce model securityLab : Design and build tabular modelsLab : Create calculation groupsModule 6: Optimize enterprise-scale tabular modelsThis module covers key aspects of performance optimization using large-format data. Students will explore optimization using Synapse, Power BI, and external tools.Lessons

• Optimize performance using Synapse and Power BI
• Improve query performance with hybrid tables, dual storage mode, and aggregations
• Use tools to optimize Power BI performance

Lab : Use tools to optimize Power BI performanceLab : Improve query performance using aggregationsLab : Improve query performance with dual storage modeLab : Improve performance with hybrid tablesModule 7: Implement advanced data visualization techniques by using Power BIThis module explores data visualization concepts including accessibility, customization of core data models, real-time data visualization, and paginated reporting.Lessons

• Understand advanced data visualization concepts
• Customize core data models
• Monitor data in real-time with Power BI
• Create and distribute paginated reports in Power BI report builder

Lab : Monitor data in real-time with Power BILab : Create and distribute paginated reports in Power BI Report BuilderModule 8: Implement and manage an analytics environmentThis module explores key considerations for implementing and managing Power BI. Students will understand key recommendations for administration and monitoring of Power BI, including configuration and management of Power BI capacity.Lessons

• Recommend Power BI administration settings
• Recommend a monitoring and auditing solution for a data analytics environment
• Configure and manage Power BI capacity
• Establish a data access infrastructure in Power BI

Module 9: Manage the analytics development lifecycleThis module explores considerations for deployment, source control, and application lifecycle management of analytics solutions. Students will understand what to recommend and will be able to deploy and manage automated and reusable Power BI assets.Lessons

• Recommend a deployment strategy for Power BI assets
• Recommend a source control strategy for Power BI assets
• Perform impact analysis of downstream dependencies from dataflows and datasets
• Recommend automation solutions for the analytics development lifecycle, including Power BI REST API
• Deploy and manage datasets by using the XMLA endpoint
• Deploy reusable assets

Lab : Create reusable Power BI assetsModule 10: Integrate an analytics platform into an existing IT infrastructureThis module explores the integration of a Power BI analytics solution into existing Azure infrastructure. Students will understand Power BI tenant and workspace configurations, along with considerations for Power BI deployment in an organization.Lessons

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