Module 1: Introduction to data analytics on Azure
This module explores key concepts of data analytics, including types of analytics, data, and storage. Students will explore the analytics process and tools used to discover insights and learn about the responsibilities of an enterprise data analyst and what tools are available to build scalable solutions.
Explore Azure data services for modern analytics
Understand concepts of data analytics
Explore data analytics at scale
Module 2: Govern data across an enterprise
This module explores the role of an enterprise data analyst in organisational data governance. Students will explore the use of Microsoft Purview to register and catalog data assets, to discover trusted assets for reporting, and to scan a Power BI environment.
Introduction to Microsoft Purview
Discover trusted data using Microsoft Purview
Catalog data artifacts by using Microsoft Purview
Manage Power BI artifacts by using Microsoft Purview
Module 3: Model, query, and explore data in Azure Synapse
This module explores the use of Azure Synapse Analytics for exploratory data analysis. Students will explore the capabilities of Azure Synapse Analytics including the basics of data warehouse design, querying data using T-SQL, and exploring data using Spark notebooks.
Introduction to Azure Synapse Analytics
Implement star schema design and query relational data in Azure
Analyse data with a serverless SQL pool in Azure Synapse Analytics
Optimise data warehouse query design
Analyse data with a Spark Pool in Azure Synapse Analytics
Lab : Query data in Azure
Lab : Explore data in Spark notebooks
Lab : Create a star schema model
Module 4: Prepare data for tabular models in Power BI
This module explores the fundamental elements of preparing data for scalable analytics solutions using Power BI. Students will explore model frameworks, considerations for building data models that will scale, Power Query optimisation techniques, and the implementation of Power BI dataflows.
Choose a Power BI model framework
Understand scalability in Power BI
Optimise Power Query for scalable solutions
Create and manage scalable Power BI dataflows
Lab : Create a dataflow
Module 5: Design and build scalable tabular models
This module explores the critical underlying aspects of tabular modeling for building Power BI models that can scale. Students will learn about model relationships and model security, working with direct query, and using calculation groups.
Lab : Create model relationships
Lab : Enforce model security
Lab : Design and build tabular models
Lab : Create calculation groups
Module 6: Optimise enterprise-scale tabular models
This module covers key aspects of performance optimisation using large-format data. Students will explore optimisation using Synapse, Power BI, and external tools.
Optimise performance using Synapse and Power BI
Improve query performance with hybrid tables, dual storage mode, and aggregations
Use tools to optimise Power BI performance
Lab : Use tools to optimise Power BI performance
Lab : Improve query performance using aggregations
Lab : Improve query performance with dual storage mode
Lab : Improve performance with hybrid tables
Module 7: Implement advanced data visualisation techniques by using Power BI
This module explores data visualisation concepts including accessibility, customisation of core data models, real-time data visualisation, and paginated reporting.
Understand advanced data visualisation concepts
Customise core data models
Monitor data in real-time with Power BI
Create and distribute paginated reports in Power BI report builder
Lab : Monitor data in real-time with Power BI
Lab : Create and distribute paginated reports in Power BI Report Builder
Module 8: Implement and manage an analytics environment
This module explores key considerations for implementing and managing Power BI. Students will understand key recommendations for administration and monitoring of Power BI, including configuration and management of Power BI capacity.
Recommend Power BI administration settings
Recommend a monitoring and auditing solution for a data analytics environment
Configure and manage Power BI capacity
Establish a data access infrastructure in Power BI
Module 9: Manage the analytics development lifecycle
This module explores considerations for deployment, source control, and application lifecycle management of analytics solutions. Students will understand what to recommend and will be able to deploy and manage automated and reusable Power BI assets.
Recommend a deployment strategy for Power BI assets
Recommend a source control strategy for Power BI assets
Perform impact analysis of downstream dependencies from dataflows and datasets
Recommend automation solutions for the analytics development lifecycle, including Power BI REST API
Deploy and manage datasets by using the XMLA endpoint
Deploy reusable assets
Lab : Create reusable Power BI assets
Module 10: Integrate an analytics platform into an existing IT infrastructure
This module explores the integration of a Power BI analytics solution into existing Azure infrastructure. Students will understand Power BI tenant and workspace configurations, along with considerations for Power BI deployment in an organisation.
Recommend and configure a Power BI tenant or workspace
Identify requirements for a solution, including features, performance, and licensing strategy
Integrate an existing Power BI workspace into Azure Synapse Analytics