DP-500T00: Designing & Implementing Enterprise Analytics Solutions

Course Description

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.

Audience Profile

Candidates for this course should have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions. Specifically, candidates should have advanced Power BI skills, including managing data repositories and data processing in the cloud and on-premises, along with using Power Query and Data Analysis Expressions (DAX). They should also be proficient in consuming data from Azure Synapse Analytics and should have experience querying relational databases, analyzing data by using Transact-SQL (T-SQL), and visualizing data.

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.

Implement and manage a data analytics environment (25–30%)

Govern and administer a data analytics environment

Manage Power BI assets by using Microsoft Purview
Identify data sources in Azure by using Microsoft Purview
Recommend settings in the Power BI admin portal
Recommend a monitoring and auditing solution for a data analytics environment, including Power BI REST API and PowerShell cmdlets

Integrate an analytics platform into an existing IT infrastructure

Identify requirements for a solution, including features, performance, and licensing strategy
Configure and manage Power BI capacity
Recommend and configure an on-premises gateway in Power BI
Recommend and configure a Power BI tenant or workspace to integrate with Azure Data Lake Storage Gen2
Integrate an existing Power BI workspace into Azure Synapse Analytics

Manage the analytics development lifecycle

Commit code and artifacts to a source control repository in Azure Synapse Analytics
Recommend a deployment strategy for Power BI assets
Recommend a source control strategy for Power BI assets
Implement and manage deployment pipelines in Power BI
Perform impact analysis of downstream dependencies from dataflows and datasets
Recommend automation solutions for the analytics development lifecycle, including Power BI REST API and PowerShell cmdlets
Deploy and manage datasets by using the XMLA endpoint
Create reusable assets, including Power BI templates, Power BI data source (.pbids) files, and shared datasets

Query and transform data (20–25%)

Query data by using Azure Synapse Analytics

Identify an appropriate Azure Synapse pool when analyzing data
Recommend appropriate file types for querying serverless SQL pools
Query relational data sources in dedicated or serverless SQL pools, including querying partitioned data sources
Use a machine learning PREDICT function in a query

Ingest and transform data by using Power BI

Identify data loading performance bottlenecks in Power Query or data sources
Implement performance improvements in Power Query and data sources
Create and manage scalable Power BI dataflows
Identify and manage privacy settings on data sources
Create queries, functions, and parameters by using the Power Query Advanced Editor
Query advanced data sources, including JSON, Parquet, APIs, and Azure Machine Learning models

Implement and manage data models (25–30%)

Design and build tabular models

Choose when to use DirectQuery for Power BI datasets
Choose when to use external tools, including DAX Studio and Tabular Editor 2
Create calculation groups
Write calculations that use DAX variables and functions, for example handling blanks or errors, creating virtual relationships, and working with iterators
Design and build a large format dataset
Design and build composite models, including aggregations
Design and implement enterprise-scale row-level security and object-level security

Optimize enterprise-scale data models

Identify and implement performance improvements in queries and report visuals
Troubleshoot DAX performance by using DAX Studio
Optimize a data model by using Tabular Editor 2
Analyze data model efficiency by using VertiPaq Analyzer
Implement incremental refresh (including the use of query folding)
Optimize a data model by using denormalization

Explore and visualize data (20–25%)

Explore data by using Azure Synapse Analytics

Explore data by using native visuals in Spark notebooks
Explore and visualize data by using the Azure Synapse SQL results pane

Visualize data by using Power BI

Create and import a custom report theme
Create R or Python visuals in Power BI
Connect to and query datasets by using the XMLA endpoint
Design and configure Power BI reports for accessibility
Enable personalized visuals in a report
Configure automatic page refresh
Create and distribute paginated reports in Power BI Report Builder

This will be a virtual event hosted on Microsoft Teams. In the Microsoft Teams platform and sessions, your name, email address, or title may be viewable by other participants. By joining this event, you agree to this experience. 

DP-500T00: Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI