A data entity in D365 is an abstraction from the physical implementation of database tables. For example, in normalized tables, a lot of the data for each customer might be stored in a customer table, and then the rest might be spread across a small set of related tables. In this case, the data entity for the customer concept appears as one de-normalized view, in which each row contains all the data from the customer table and its related tables.
Data Entity Categories
Functional or behavioral parameters. Required to set up a deployment or a module for a specific build or customer. Can include data that is specific to an industry or business. The data can also apply to broader set of customers. Tables that contain only one record, where the columns are values for settings. Examples of such tables exist for Account payable (AP), General ledger (GL), client performance options, workflows, and so on.
Simple reference data, of small quantity, that is required to operate a business process.
Data that is specific to an industry or a business process. Examples include units, dimensions, and tax codes.
Data assets of the business. Generally, these are the “nouns” of the business, which typically fall into categories such as people, places, and concepts. Complex reference data, of large quantity. Examples include customers, vendors, and projects.
Worksheet data that is converted into transactions later. Documents that have complex structures, such a several line items for each header record. Examples include sales orders, purchase orders, open balances, and journals.
The operational transaction data of the business. Posted transactions. These are non‑idempotent items such as posted invoiced and balances. Typically, these items are excluded during a full dataset copy. Examples include pending invoices.
Data Entity Use Cases
Synchronous service (OData)
Data entities enable public application programming interfaces (APIs) on entities to be exposed, which enables synchronous services. Synchronous services are used for the following purposes:
• Office integration
• Third-party mobile apps
Data entities also support asynchronous integration through a data management pipeline. This enables asynchronous and high-performing data insertion and extraction scenarios. Here are some examples:
• Interactive file-based import/export
• Recurring integrations (file, queue, and so on)