Overview
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.
Prerequisites
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 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.
Course Duration:
5 Days
Course Outline
- 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
- 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 Azure 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
- Exercise – Explore Azure Synapse Analytics
- 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
- 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
- Exercise – Explore 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
- Exercise: Create a dataflow
- Use DAX time intelligence functions
- Additional time intelligence calculations
- Understand calculation groups
- Explore calculation groups features and usage
- Create calculation groups in a model
- Exercise: Create calculation groups
- Restrict access to Power BI model data
- Restrict access to Power BI model objects
- Apply good modeling practices
- Exercise: Enforce model security
- 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
- 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
- Exercise: Monitor data in real-time with Power BI
- 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
- 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
- Usage metrics for dashboards and reports
- Usage metrics for dashboards and reports – new version
- Audit logs
- Activity log
- Premium resource management
- Supporting multi geographies
- Bring your own key (BYOK)
- 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
- 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
- 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
- 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
- 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
- Exercise: Create reusable Power BI assets
