DP-500T00 Designing and Implementing Enterprise-Scale Analytics Using Azure and Power BI
1 – Explore Azure data services for modern analytics
– Understand the Azure data ecosystem
– Explore modern analytics solution architecture
2 – Understand concepts of data analytics
– Understand data analytics types
– Explore the data analytics process
– Understand types of data and data storage
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
4 – Introduction to Microsoft Purview
– What is Microsoft Purview?
– How Microsoft Purview works
– When to use Microsoft Purview
5 – Discover trusted data using Microsoft Purview
– Search for assets
– Browse assets
– Use assets with Power BI
– Integrate with Azure Synapse Analytics
6 – Catalog data artifacts by using Microsoft Purview
– Register and scan data
– Classify and label data
– Search the data catalog
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
8 – Integrate Microsoft Purview and Azure Synapse Analytics
– Catalog Azure Synapse Analytics data assets in Microsoft Purview
– Connect Microsoft Purview to an Azure Synapse Analytics workspace
– Search a Purview catalog in Synapse Studio
– Track data lineage in pipelines
9 – Introduction to Azure Synapse Analytics
– What is Azure Synapse Analytics
– How Azure Synapse Analytics works
– When to use Azure Synapse Analytics
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
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
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
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
14 – Understand scalability in Power BI
– Describe the significance of scalable models
– Implement Power BI data modeling best practices
– Configure large datasets
15 – Create and manage scalable Power BI dataflows
– Define use cases for dataflows
– Create reusable assets
– Implement best practices
16 – Create Power BI model relationships
– Understand model relationships
– Set up relationships
– Use DAX relationship functions
– Understand relationship evaluation
17 – Use DAX time intelligence functions in Power BI Desktop models
– Use DAX time intelligence functions
– Additional time intelligence calculations
18 – Create calculation groups
– Understand calculation groups
– Explore calculation groups features and usage
– Create calculation groups in a model
19 – Enforce Power BI model security
– Restrict access to Power BI model data
– Restrict access to Power BI model objects
– Apply good modeling practices
20 – 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
21 – 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
22 – 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
23 – Create paginated reports
– Get data
– Create a paginated report
– Work with charts on the report
– Publish the report
24 – Provide governance in a Power BI environment
– Elements of data governance
– Configure tenant settings
– Deploy organizational visuals
– Manage embed codes
– Help and support settings
25 – Monitor and audit usage
– Usage metrics for dashboards and reports
– Usage metrics for dashboards and reports – new version
– Audit logs
– Activity log
26 – 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
– Template app governance
27 – Build reports using Power BI within Azure Synapse Analytics
– Describe the Power BI and Synapse workspace integration
– Understand Power BI data sources
– Describe Power BI optimization options
– Visualize data with serverless SQL pools
28 – Design a Power BI application lifecycle management strategy
– Define application lifecycle management
– Recommend a source control strategy
– Design a deployment strategy
29 – 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
30 – 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