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Microsoft Data & Power BIDP-500T00 Designing and Implementing Enterprise-Scale Analytics Using Azure and Power BI
Design enterprise-scale analytics with Azure and Power BI.
4 daysIntermediate30 modulesDP-500T00Microsoft Certified: Azure Enterprise Data Analyst Associate
About this course
Design enterprise-scale analytics with Azure and Power BI. Over four days you will catalogue and govern data with Microsoft Purview, query a data lake with Azure Synapse serverless SQL, analyse data with Apache Spark, build a relational data warehouse and design scalable Power BI models, preparing for DP-500.
Curriculum
30 modules
- Understand the Azure data ecosystem
- Explore modern analytics solution architecture
- Understand data analytics types
- Explore the data analytics process
- Understand types of data and data storage
- 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
- What is Microsoft Purview?
- How Microsoft Purview works
- When to use Microsoft Purview
- Search for assets
- Browse assets
- Use assets with Power BI
- Integrate with Azure Synapse Analytics
- Register and scan data
- Classify and label data
- Search the data catalog
- Register and scan a Power BI tenant
- Search and browse Power BI assets
- View Power BI metadata and lineage
- 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
- What is Azure Synapse Analytics
- How Azure Synapse Analytics works
- When to use Azure Synapse Analytics
- Understand Azure Synapse serverless SQL pool capabilities and use cases
- Query files using a serverless SQL pool
- Create external database objects
- Get to know Apache Spark
- Use Spark in Azure Synapse Analytics
- Analyze data with Spark
- Visualize data with Spark
- Design a data warehouse schema
- Create data warehouse tables
- Load data warehouse tables
- Query a data warehouse
- 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
- Describe the significance of scalable models
- Implement Power BI data modeling best practices
- Configure large datasets
- Define use cases for dataflows
- Create reusable assets
- Implement best practices
- Understand model relationships
- Set up relationships
- Use DAX relationship functions
- Understand relationship evaluation
- Use DAX time intelligence functions
- Additional time intelligence calculations
- Understand calculation groups
- Explore calculation groups features and usage
- Create calculation groups in a model
- Restrict access to Power BI model data
- Restrict access to Power BI model objects
- Apply good modeling practices
- Use Performance analyzer
- Troubleshoot DAX performance by using DAX Studio
- Optimize a data model by using Best Practice Analyzer
- 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
- Describe Power BI real-time analytics
- Set up automatic page refresh
- Create real-time dashboards
- Set-up auto-refresh paginated reports
- Get data
- Create a paginated report
- Work with charts on the report
- Publish the report
- Elements of data governance
- Configure tenant settings
- Deploy organizational visuals
- Manage embed codes
- Help and support settings
- Usage metrics for dashboards and reports
- Usage metrics for dashboards and reports - new version
- Audit logs
- Activity log
- 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
- Describe the Power BI and Synapse workspace integration
- Understand Power BI data sources
- Describe Power BI optimization options
- Visualize data with serverless SQL pools
- Define application lifecycle management
- Recommend a source control strategy
- Design a deployment strategy
- Understand the deployment process
- Create a deployment pipeline
- Assign a workspace
- Deploy content
- Work with deployment pipelines
- Create reusable Power BI assets
- Explore Power BI assets using lineage view
- Manage a Power BI dataset using XMLA endpoint
Who it's for
Experienced data analysts and BI architects.
Before you start
- A foundational knowledge of core data concepts and how they’re implemented using Azure data services. For more information see 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. For more information see Power BI Data Analyst.
Prepares for
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