As businesses look to modernize their line of business applications Microsoft Power Platform is the tool of choice to accelerate and implement digital transformation. Microsoft Power Platform is made up of Power Apps, Power Automate, Power BI and Power Virtual Agents. The Common Data Service (CDS) is a key component of the Power Platform and provides for data storage and related business logic execution. CDS implements Microsoft’s Common Data Model (CDM) allowing it to be part of the data fabric in an organization with a common way of describing core business data. In this course, we are focused on using the Common Data Service to build business applications. Specifically, we will look at the capabilities required to build data models for real world business applications. These applications can be built by power users (e.g. Joe or Sue from accounting) or by professional app makers whose job is focused on building Power Platform solutions.
What is the Common Data Service (CDS) and when to use it as part of your data and business logic strategy. This includes understanding the core capabilities, architecture and high level licensing.
Understand how the Microsoft Common Data Model is used by CDS and how you should leverage it when building your CDS data model.
Entities are the key building blocks for storing data in CDS. Learn about the different types of entities, ownership and how to decide between custom and CDM entity definitions.
Each CDS entity is a collection of fields that store the individual data elements. Each field has a data type. Learn about all the different CDS data types and how to choose the best one for your data.
Relationships bring real world data models to life by describing the connections between business data (entities). This chapter explores how to use relationships and to select the right relationship behaviors based on your data and business requirements.
At the heart of any good line of business application is the data model. CDS allows app builders to declaratively build the data model and work with it using all the power of the platform. This chapter is a must-read for a good understanding of the decisions made in the remaining chapters.
There is no one size fits all for data modeling so one of the best ways to learn about it is via scenarios and challenges. In this collection each contains a scenario to convey approaches for data modeling of a specific problem or technique.