This course covers methods and practices for performing advanced data analytics at
scale. Students will build on existing analytics experience and 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 course, students will use
Microsoft Purview, Azure Synapse Analytics, and Power BI to build analytics solutions.

Course dates and course fee may be subjected to changes.

Course Outline

Lesson 1: Explore Azure data services for modern analytics

• Understand the Azure data ecosystem
• Explore modern analytics solution architecture

Lesson 2: Understand concepts of data analytics

• Understand data analytics types
• Explore the data analytics process
• Understand types of data and data storage

Lesson 3: Explore data analytics at scale

• Explore data team roles and responsibilities
• Review tasks and tools for data analysts
• Scale analytics with Azure Synapse Analytics and Power BI
• Strategies to scale analytics

Lesson 4: Introduction to Microsoft Purview

• What is Microsoft Purview?
• How Microsoft Purview works
• When to use Microsoft Purview

Lesson 5: Discover trusted data using Microsoft Purview

• Search for assets
• Browse assets
• Use assets with Power BI
• Integrate with Azure Synapse Analytics

Lesson 6: Catalog data artifacts by using Microsoft Purview

• Register and scan data
• Classify and label data

Lesson 7: Manage Power BI assets by using Microsoft Purview

• Register and scan a Power BI tenant
• Search and browse Power BI assets
• View Power BI metadata and lineage

Lesson 8: Integrate Microsoft Purview and Azure Synapse Analytics

• Catalog Azure Synapse Analytics data assets in Microsoft Purview
• Connect Azure purview to an Azure Synapse Analytics workspace
• Search a Purview catalog in Synapse Studio
• Track data lineage in pipelines

Lesson 9: Introduction to Azure Synapse Analytics

• What is Azure Synapse Analytics
• How Azure Synapse Analytics works
• When to use Azure Synapse Analytics
• Exercise – Explore Azure Synapse Analytics

Lesson 10: Use Azure Synapse serverless SQL pool to query files in a data lake

• Understand Azure Synapse serverless SQL pool capabilities and use cases
• Query files using a serverless SQL pool
• Create external database objects
• Exercise – Query files using a serverless SQL pool

Lesson 11: Analyze data with Apache Spark in Azure Synapse Analytics

• Get to know Apache Spark
• Use Spark in Azure Synapse Analytics
• Analyze data with Spark
• Visualize data with Spark

Lesson 12: Analyze data in a relational data warehouse

• Design a data warehouse schema
• Create data warehouse tables
• Load data warehouse tables
• Query a data warehouse
• Exercise – Explore a data warehouse

Lesson 13: Choose a Power BI model framework

• Describe Power BI model fundamentals
• Determine when to develop an import model
• Determine when to develop a DirectQuery model
• Determine when to develop a composite model
• Choose a model framework

Lesson 14: Understand scalability in Power BI

• Describe the significance of scalable models
• Implement Power BI data modeling best practices
• Configure large datasets

Lesson 15: Create and manage scalable Power BI dataflows

• Define use cases for dataflows
• Create reusable assets
• Implement best practices
• Exercise: Create a dataflow

Lesson 16: Use DAX time intelligence functions in Power BI Desktop models

• Use DAX time intelligence functions
• Additional time intelligence calculations

Lesson 17: Create calculation groups

• Understand calculation groups
• Explore calculation groups features and usage
• Create calculation groups in a model
• Exercise: Create calculation groups

Lesson 18: Enforce Power BI model security

• Restrict access to Power BI model data
• Restrict access to Power BI model objects
• Apply good modeling practices
• Exercise: Enforce model security

Lesson 19: Use tools to optimize Power BI performance

• 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

Lesson 20: Understand advanced data visualization concepts

• Create and import a custom report theme
• Enable personalized visuals in a report
• Design and configure Power BI reports for accessibility
• Create custom visuals with R or Python
• Review report performance using Performance Analyzer

Lesson 21: Monitor data in real-time with Power BI

• Describe Power BI real-time analytics
• Set up automatic page refresh
• Create real-time dashboards
• Set-up auto-refresh paginated reports
• Exercise: Monitor data in real-time with Power BI

Lesson 22: Create paginated reports

• Get data
• Create a paginated report
• Work with charts on the report
• Publish the report

Lesson 23: Provide governance in a Power BI environment

• Elements of data governance
• Configure tenant settings
• Deploy organizational visuals
• Manage embed codes

Lesson 24: Facilitate collaboration and sharing in Power BI

• Workspaces evolved
• Impact to Power BI users
• Permissions in workspaces v2
• Apps in Power BI
• Share
• Publish to web
• Embed and link in portals
• Data sensitivity labels
• Data privacy

Lesson 25: Monitor and audit usage

• Usage metrics for dashboards and reports
• Usage metrics for dashboards and reports – new version
• Audit logs
• Activity log

Lesson 26: Provision Premium capacity in Power BI

• Premium resource management
• Supporting multi geographies
• Bring your own key (BYOK)

Lesson 27: Establish a data access infrastructure in Power BI

• Personal gateways versus enterprise gateways
• How data is refreshed
• Gateway network requirements
• Where to install gateway?
• Establish high availability gateways
• Establish load balancing of gateways
• Gateway performance monitoring documentation
• Multiple data sources per gateway
• Manage gateway users
• Active Directory user mapping with custom property lookup

Lesson 28: Broaden the reach of Power BI

• REST API custom development
• Provision a Power BI embedded capacity
• Dataflow introduction
• Dataflow explained
• Create a Dataflow
• Dataflow capabilities on Power BI Premium
• Template apps – install packages
• Template apps – installed entities

Lesson 29: Automate Power BI administration

• REST API – Power BI service
• Microsoft Power BI cmdlets for Windows PowerShell and PowerShell core
• Install and use the Power BI cmdlet
• Test REST API calls
• Script typical administrator tasks
• Lab

Lesson 30: Build reports using Power BI within Azure Synapse Analytics

• Describe the Power BI and Synapse workspace integration
• Exercise – Connect to Power BI from Synapse
• Understand Power BI data sources
• Exercise – Create a new data source to use in Power BI
• Exercise – Create a new Power BI report in Synapse Studio
• Describe Power BI optimization options
• Exercise – Improve performance with materialized views and result-set caching
• Visualize data with serverless SQL pools

Lesson 31: Design a Power BI application lifecycle management strategy

• Define application lifecycle management
• Recommend a source control strategy
• Design a deployment strategy

Lesson 32: Create and manage a Power BI deployment pipeline

• Understand the deployment process
• Create a deployment pipeline
• Assign a workspace
• Deploy content
• Work with deployment pipelines

Lesson 33: Create and manage Power BI assets

• Create reusable Power BI assets
• Explore Power BI assets using lineage view
• Manage a Power BI dataset using XMLA endpoint
• Exercise: Create reusable Power BI assets