Training: Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI (DP-500)

Ref. DP-500T00

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Duration:

4

days

Exam:

Optional

Level:

Intermediate

Funding:

Temptraining

Home > Trainings > IT Pro > Analytics and Data Management > Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI (DP-500)

Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI (DP-500)

This training, Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI (DP-500), covers the methods and practices for performing advanced data analysis at scale. Students will build on their existing analytics experience and 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, Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI, participants will use Microsoft Purview, Azure Synapse Analytics, and Power BI to create analytics solutions.

Participant profiles

Solution architects
Data engineers
Data scientists
AI engineers
Database administrators
Power BI data analysts

Objectives

Implement and manage a data analytics environment
Query and transform data
Implement and manage data models
Explore and visualise data

Prerequisites

A foundational knowledge of core data concepts and how they’re implemented using Azure data services
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

Course content

Module 1: Explore Azure data services for modern analytics

Describe the Azure data ecosystem for analytics

Module 2: Understand concepts of data analytics

Describe types of data analytics
Understand the data analytics process

Module 3: Explore data analytics at scale

Explore data job roles in analytics
Understand tools for scaling analytics solutions

Module 4: Introduction to Microsoft Purview

Evaluate whether Microsoft Purview is appropriate for 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

Browse, search, and manage data catalog assets
Use data catalog assets with Power BI
Use Microsoft Purview in Azure Synapse

Module 6: Catalog data artifacts by using Microsoft Purview

Describe asset classification in Microsoft Purview

Module 7: Manage Power BI assets by using Microsoft Purview

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

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

Identify the business problems that Azure Synapse Analytics addresses
Describe 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

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: Analyze data with Apache Spark in Azure Synapse Analytics

Identify core features and capabilities of Apache Spark
Configure a Spark pool in Azure Synapse Analytics
Run code to load, analyze, and visualize data in a Spark notebook

Module 12: Analyze data in a relational data warehouse

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 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

Describe the importance of building scalable data models
Implement Power BI data modeling best practices
Use the Power BI large dataset storage format

Module 15: Create and manage scalable Power BI dataflows

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

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

Define time intelligence
Use common DAX time intelligence functions
Create useful intelligence calculations

Module 18: Create calculation groups

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

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 optimize Power BI performance

Optimize queries using performance analyzer
Troubleshoot DAX performance using DAX Studio
Optimize a data model using Tabular Editor

Module 21: Understand advanced data visualization concepts

Create and import a custom report theme
Create custom visuals with R or Python
Enable personalized visuals in a report
Review report performance using Performance Analyzer
Design and configure Power BI reports for accessibility

Module 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

Module 23: Create paginated reports

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

Define the key components of an effective BI governance model
Describe the key 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

Understand the differences between My workspace, workspaces, and apps
Describe new workspace capabilities and how they improve the user experience
Anticipate migration impact to Power BI users
Share, publish to the web, embed links and secure Power BI reports, dashboards, and content

Module 26: Monitor and audit usage

Discover what usage metrics are available through the Power BI admin portal
Optimize use of usage metrics for dashboards and reports
Distinguish between audit logs and the activity logs

Module 27: Provision Premium capacity in Power BI

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

Understand the difference between gateways, the various 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

Describe the various embedding scenarios that allow you to broaden the reach of Power BI
Understand the options for developers to customize Power BI solutions
Learn to provision and optimize Power BI embedded capacity and create and deploy dataflows
Build custom Power BI solutions template apps

Module 30: Automate Power BI administration

Use REST APIs to automate common Power BI admin tasks
Apply Power BI Cmdlets for Windows PowerShell and PowerShell core
Use Power BI Cmdlets
Automate common Power BI admin tasks with scripting

Module 31: Build reports using Power BI within Azure Synapse Analytics

Describe the Power BI and Synapse workspace integration
Understand Power BI data sources
Describe optimization options
Visualize data with serverless SQL pools

Module 32: Design a Power BI application lifecycle management strategy

Outline the application lifecycle process
Choose a source control strategy
Design a deployment strategy

Module 33: Create and manage a Power BI deployment pipeline

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

Create specialized datasets
Create live and DirectQuery connections
Use Power BI service lineage view
Use XMLA endpoint to connect datasets

Documentation

Access to Microsoft Learn (online learning content)

Lab / Exercises

Official Microsoft Labs

Exam

This course prepares you to the DP-500: Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI exam
If you wish to take this exam, please select it when you add the course to your basket

Complementary courses

Temptraining funding

ITTA is a partner of Temptraining, the continuing education fund for temporary workers. This training fund can subsidize continuing education for anyone who works for an employer subject to the Collective Work Agreement (CCT) Rental of services.

Additional information

The training Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI (DP-500) is designed to equip you with the skills necessary to create large-scale data analytics solutions. You will learn to harness the power of Azure data services and Power BI tools to transform data into actionable insights.

Fundamentals of Azure Data Services

Overview of the Azure Data Ecosystem

The Azure data ecosystem is a comprehensive suite of cloud services designed to meet modern analytics needs. Azure Synapse Analytics, Azure Data Lake Storage, and Azure Databricks are some of the key services that enable efficient management, analysis, and visualization of large volumes of data.

Architecture of Modern Analytics Solutions

The architecture of modern analytics solutions relies on the seamless integration of various Azure services to create robust data pipelines. This includes data collection, storage, processing, and visualization, enabling informed real-time decision-making.

Exploring Data Analytics Concepts

Types and Processes of Data Analytics

There are several types of data analytics, such as descriptive, predictive, and prescriptive analytics. Understanding these types helps you choose the right approach for your specific needs. The data analytics process involves collecting data, cleaning it, transforming it, and finally analyzing it to gain valuable insights.

Data Storage and Management

Data storage is a crucial component of any analytics solution. Azure offers various storage options, such as Azure SQL Database and Azure Cosmos DB, allowing for efficient management of structured and unstructured data.

Scaling Data Analytics

Roles and Responsibilities in the Data Team

A typical data team includes various roles, ranging from data analysts to data engineers and data scientists. Each plays a specific role in the data analytics lifecycle.

Scaling Strategies with Azure Synapse and Power BI

Azure Synapse Analytics and Power BI are powerful tools for scaling your analytics. Azure Synapse enables seamless integration with other Azure services, while Power BI offers advanced visualization and reporting capabilities.

Data Discovery and Governance with Microsoft Purview

Using Microsoft Purview for Data Management

Microsoft Purview is a data governance solution that allows for effective data discovery, classification, and management. It facilitates resource search and integration with other tools like Power BI and Azure Synapse Analytics.

Analyzing Large-Scale Data with Azure Synapse Analytics

Features of Azure Synapse Analytics

Azure Synapse Analytics combines data integration, data warehousing, and big data analytics. It allows you to create robust data pipelines and perform large-scale analytics.

Performance Optimization and Data Visualization with Power BI

Principles of Modeling and Scalability in Power BI

Data modeling in Power BI is essential for creating performant and scalable reports. By following best practices, you can optimize the performance of your datasets and create impactful visualizations.

Creating Dataflows and Paginated Reports

Power BI enables the creation of reusable dataflows and paginated reports that facilitate the management and distribution of insights across the organization.

Automating and Governing Power BI Solutions

Automating Administrative Tasks with REST APIs

Automating administrative tasks with Power BI REST APIs saves time and ensures efficient management of Power BI resources. You can write scripts to automate repetitive tasks and manage deployments smoothly.

Real-Time Monitoring and Collaboration with Power BI

Real-Time Analytics and Collaboration

Real-time data monitoring with Power BI allows you to respond quickly to changes and collaborate effectively within teams. Real-time dashboards and paginated reports facilitate the sharing of insights and the optimization of decision-making processes.

The DP-500 training equips you with the skills necessary to design and implement large-scale analytics solutions using Microsoft Azure and Power BI. By mastering these tools, you can transform raw data into actionable insights, driving your organization towards smarter and faster decision-making.