In this Designing & Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure & Power-BI (DP-500) course, you will learn how to:
Implement and manage a data analytics environment
Query and transform data
Implement and manage data models
Explore and visualise data
In-Person
Online
Module 1: Explore Azure data services for modern analytics
Understand analytics solutions in the Azure data ecosystem. Explore the architecture of a scalable analytics solution to meet business needs.
After completing this module, you will be able to:
Describe the Azure data ecosystem for analytics
Module 2: Understand concepts of data analytics
Explore critical data analytics concepts, including types of analytics, data, and storage. Explore the analytics process and tools used to discover insights.
After completing this module, you will be able to:
Describe types of data analytics
Understand the data analytics process
Module 3: Explore data analytics at scale
Describe data analytics at scale and understand the roles of a data team. Learn about the responsibilities of an enterprise data analyst and what tools are available to build scalable solutions.
After completing this module, you will be able to:
Explore data job roles in analytics
Understand tools for scaling analytics solutions
Module 4: Introduction to Microsoft Purview
In this module, you'll evaluate whether Microsoft Purview is the right choice for your data discovery and governance needs.
By the end of this module, you'll be able to:
Evaluate whether Microsoft Purview is appropriate for your data discovery and governance needs.
Describe how the features of Microsoft Purview work to provide data discovery and governance.
Module 5: Discover trusted data using Microsoft Purview
Use Microsoft Purview Studio to discover trusted organisational assets for reporting.
After completing this module, you'll be able to:
Browse, search, and manage data catalogue assets.
Use data catalogue assets with Power BI.
Use Microsoft Purview in Azure Synapse Studio.
Module 6: Catalog data artefacts by using Microsoft Purview
Register, scan, catalog, and view data assets and their relevant details in Microsoft Purview.
By the end of this module, you’ll be able to:
Describe asset classification in Microsoft Purview.
Module 7: Manage Power BI assets by using Microsoft Purview
Improve data governance and asset discovery using Power BI and Microsoft Purview integration.
By the end of this module, you’ll be able to:
Register and scan a Power BI tenant.
Use the search and browse functions to find data assets.
Describe the schema details and data lineage tracing of Power BI data assets.
Module 8: Integrate Microsoft Purview and Azure Synapse Analytics
Learn how to integrate Microsoft Purview with Azure Synapse Analytics to improve data discoverability and lineage tracking.
After completing this module, you'll be able to:
Catalog Azure Synapse Analytics database assets in Microsoft Purview.
Configure Microsoft Purview integration in Azure Synapse Analytics.
Search the Microsoft Purview catalog from Synapse Studio.
Track data lineage in Azure Synapse Analytics pipelines activities
Module 9: Introduction to Azure Synapse Analytics
Learn about the features and capabilities of Azure Synapse Analytics - a cloud-based platform for big data processing and analysis.
In this module, you'll learn how to:
Identify the business problems that Azure Synapse Analytics addresses.
Describe the core capabilities of Azure Synapse Analytics.
Determine when to use Azure Synapse Analytics.
Module 10: Use Azure Synapse serverless SQL pool to query files in a data lake
With Azure Synapse serverless SQL pool, you can leverage your SQL skills to explore and analyse data in files without the need to load the data into a relational database.
After the completion of this module, you will be able to:
Identify capabilities and use cases for serverless SQL pools in Azure Synapse Analytics
Query CSV, JSON, and Parquet files using a serverless SQL pool
Create external database objects in a serverless SQL pool
Module 11: Analyse data with Apache Spark in Azure Synapse Analytics
Apache Spark is a core technology for large-scale data analytics. Learn how to use Spark in Azure Synapse Analytics to analyse and visualise data in a data lake.
After completing this module, you will be able to:
Identify core features and capabilities of Apache Spark.
Configure a Spark pool in Azure Synapse Analytics.
Run code to load, analyse, and visualise data in a Spark notebook.
Module 12: Analyze data in a relational data warehouse
Relational data warehouses are a core element of most enterprise Business Intelligence (BI) solutions and are used as the basis for data models, reports, and analysis.
In this module, you'll learn how to:
Design a schema for a relational data warehouse.
Create fact, dimension, and staging tables.
Use SQL to load data into data warehouse tables.
Use SQL to query relational data warehouse tables.
Module 13: Choose a Power BI model framework
Describe model frameworks, their benefits and limitations, and features to help optimise your Power BI data models.
By the end of this module, you’ll be able to:
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 an appropriate Power BI model framework.
Module 14: Understand scalability in Power BI
Scalable data models enable enterprise-scale analytics in Power BI. Implement data modelling best practices, use large dataset storage format, and practice building a star schema to design analytics solutions that can scale.
By the end of this module, you’ll be able to:
Describe the importance of building scalable data models
Implement Power BI data modelling best practices
Use the Power BI large dataset storage format
Module 15: Create and manage scalable Power BI dataflows
Create Power BI transformation logic for reuse across your organisation with Power BI dataflows. Learn how to combine Power BI dataflows with Power BI Premium for scalable ETL and practice creating and consuming dataflows.
By the end of this module, you’ll be able to:
Describe Power BI dataflows and use cases.
Describe best practices for implementing Power BI dataflows.
Create and consume Power BI dataflows.
Module 16: Create Power BI model relationships
Power BI model relationships form the basis of a tabular model. Define Power BI model relationships, set up relationships, recognise DAX relationship functions, and describe relationship evaluation.
By the end of this module, you’ll be able to:
Understand how model relationship work.
Set up relationships.
Use DAX relationship functions.
Understand relationship evaluation.
Module 17: Use DAX time intelligence functions in Power BI Desktop models
By the end of this module, you'll learn the meaning of time intelligence and how to add time intelligence DAX calculations to your model.
By the end of this module, you'll be able to:
Define time intelligence.
Use standard DAX time intelligence functions.
Create functional intelligence calculations.
Module 18: Create calculation groups
In this module, you’ll learn calculation groups, explore critical features and usage scenarios, and learn to create calculation groups.
After completing this module, you will be able to:
Explore how calculation groups work.
Maintain calculation groups in a model.
Use calculation groups in a Power BI report.
Module 19: Enforce Power BI model security
Enforce model security in Power BI using row-level security and object-level security.
By the end of this module, you’ll be able to:
Restrict access to Power BI model data with RLS.
Restrict access to Power BI model objects with OLS.
Apply good development practices to enforce Power BI model security.
Module 20: Use tools to optimise Power BI performance
Use tools to develop, manage, and optimise the Power BI data model and DAX query performance.
After completing this module, you'll be able to:
Optimise queries using a performance analyser.
Troubleshoot DAX performance using DAX Studio.
Optimise a data model using Tabular Editor.
Module 21: Understand advanced data visualisation concepts
Create cohesive, inclusive, and efficient Power BI reports to communicate results effectively.
In this module, you’ll learn how to:
Create and import a custom report theme.
Create custom visuals with R or Python.
Enable personalised visuals in a report.
Review report performance using Performance Analyser.
Design and configure Power BI reports for accessibility.
Module 22: Monitor data in real-time with Power BI
Describe real-time analytics in Power BI using automatic page refresh, real-time dashboards, and auto-refresh in paginated reports.
By the end of this module, you’ll be able to:
Describe Power BI real-time analytics.
Set up automatic page refresh.
Create real-time dashboards.
Set up auto-refresh paginated reports.
Module 23: Create paginated reports
Paginated reports allow report developers to create Power BI artefacts with tightly controlled rendering requirements. Paginated reports are ideal for creating sales invoices, receipts, purchase orders, and tabular data. This module will teach you how to create reports, add parameters, and work with tables and charts in paginated reports.
In this module, you will:
Get data.
Create a paginated report.
Work with charts and tables on the report.
Publish the report
Module 24: Provide governance in a Power BI environment
Power BI governance is a set of rules, regulations, and policies that define and ensure a BI environment's effective, controlled, and valuable operation. This module will teach you the fundamental components and practices necessary to govern a Power BI tenant.
In this module, you will:
Define the critical components of an effective BI governance model
Describe the critical elements associated with data governance
Configure, deploy and manage elements of a BI governance strategy
Set up BI help and support settings
Module 25: Facilitate collaboration and sharing in Power BI
You've created dashboards and reports. Perhaps you want to collaborate on them with your coworkers. Or maybe you're ready to distribute them more widely. What's the best way to collaborate and share them? In this module, we compare your options.
In this module, you will:
Understand the differences between My workspace, workspaces, and apps
Describe new workspace capabilities and how they improve the user experience
Anticipate migration impact on Power BI users
Share, publish to the web, embed links and secure Power BI reports, dashboards, and content
Module 26: Monitor and audit usage
Usage metrics help you understand the impact of your dashboards and reports. When you run either dashboard or report usage metrics, you discover how those dashboards and reports are used throughout your organisation, who's using them, and for what purpose. Knowing who is taking what action on which item in your Power BI tenant can be critical in helping your organisation fulfil its requirements, like meeting regulatory compliance and records management. This module outlines what is available in usage metrics reports and audit logs.
In this module, you will:
Discover what usage metrics are available through the Power BI admin portal
Optimise the use of usage metrics for dashboards and reports
Distinguish between audit logs and the activity logs
Module 27: Provision Premium capacity in Power BI
Power BI Premium is a dedicated, capacity-based offering. Learn about the differences between Power BI Pro and Power BI Premium, and how Power BI Premium manages capacity resources. Featured tools you can use with Power BI premium are also covered.
By the end of this module, you'll be able to:
Describe the difference between Power BI Pro and Power BI Premium
Define dataset eviction
Explain how Power BI manages memory resources
List three external tools you can use with Power BI Premium
Module 28: Establish a data access infrastructure in Power BI
Working with on-premises data sources requires configuring a gateway between Power BI and the on-premises data source. This module examines how to work with gateways and SQL Server Analysis Services (SSAS) data sources used for scheduled refresh or live connections.
By the end of this module, you'll be able to:
Understand the difference between gateways, connectivity modes, and data refresh methods.
Describe the gateway network requirements, where to place the gateway in your network, and how to use clustering to ensure high availability.
Scale, monitor, and manage gateway performance and users
Module 29: Broaden the reach of Power BI
You can broaden the reach of Power BI by sharing your reports beyond your Power BI environment. You can publish reports to the public internet, embed reports in Microsoft Teams or PowerApps, and place BI reports in a SharePoint online web part. There's also a particular version of Power BI service called Microsoft Power BI Embedded (PBIE). It allows application developers to embed fully interactive reports into their applications without building their own data visualisations and controls from scratch.
By the end of this module, you'll be able to:
Describe the various embedding scenarios that allow you to broaden the reach of Power BI
Understand the options for developers to customise Power BI solutions
Learn to provision and optimise Power BI embedded capacity and create and deploy dataflows
Build custom Power BI solutions template apps
Module 30: Automate Power BI administration
Cmdlets are functions written in PowerShell script language that execute commands in the Windows PowerShell environment. Running these cmdlets will allow you to interact with your Power BI Platform without going through the admin portal in a web browser. Combine these cmdlets with other PowerShell functions to write complex scripts that can optimise your workflow.
By the end of this module, you'll be able to:
Use REST APIs to automate everyday Power BI admin tasks
Apply Power BI Cmdlets for Windows PowerShell and PowerShell core
Use Power BI Cmdlets
Automate everyday Power BI admin tasks with scripting
Module 31: Build reports using Power BI within Azure Synapse Analytics
In this module, you will learn how to build Power BI reports from within Azure Synapse Analytics.
In this module, you'll:
Describe the Power BI and Synapse workspace integration
Understand Power BI data sources
Describe optimisation options
Visualise data with serverless SQL pools
Module 32: Design a Power BI application lifecycle management strategy
Using OneDrive, Git repositories, and Power BI deployment pipelines allows us to follow application lifecycle management techniques. This reduces administrative overhead and provides continuity in the development process.
Upon completion of this module, you should be able to:
Outline the application lifecycle process.
Choose a source control strategy.
Design a deployment strategy.
Module 33: Create and manage a Power BI deployment pipeline
Deployment pipelines enable creators to develop and test Power BI content in the Power BI service before the content is made available for consumption by users. It offers creators improved productivity, faster delivery of content updates, and reduced manual work and errors. The tool is designed as a pipeline with three stages: development, testing, and production.
By the end of this module, you’ll be able to:
Articulate the benefits of deployment pipelines
Create a deployment pipeline using Premium workspaces
Assign and deploy content to pipeline stages
Describe the purpose of deployment rules
Deploy content from one pipeline stage to another
Module 34: Create and manage Power BI assets
Creating shared data assets for your analytics environment provides structure and consistency. Maintaining those assets is necessary, and the XMLA endpoint provides additional administrative capabilities.
Upon completion of this module, you should be able to:
Create specialised datasets.
Create live and direct query connections.
Use Power BI service lineage view.
Use the XMLA endpoint to connect datasets.
