![]() A combination of both types of tables may be appropriate. The choice of table should be based on the specific needs of your application. Your application requires complex joins.Your application requires relational modeling and needs transactional capability across tables and during plugin execution stages.Your application requires strong consistency.You need to handle a high volume of read and write requests.Your data might be unstructured or semi-structured, or if your data model might constantly change.In addition, time-to-live capability with elastic table ( Coupon in this scenario) allows removal of data automatically after fixed periods and ensure optimization of storage capacity. Since the elastic tables are isolated from the standard tables, performance for the overall marketing application won't be negatively impacted. The requirement for Contoso's marketing application is that it must be able to ingest up to 100 million or more coupon details within a few hours, read millions of coupons per hour, and send coupons to customers.Įlastic tables will automatically scale for this high throughput scenario.įor example, in the above scenario, an elastic table named Coupon with millions of records can be associated with Dataverse standard tables like Contact (customer info) and Offer (a custom standard table). Marketing plans to run multiple 24-hour campaigns targeting different customer segments. ![]() They have estimated that the number of coupons required will be 100 million plus per flash sale campaign. Based on prior customer history, they're looking to have 24-hour flash sale events with different coupons targeting their customers and products. ![]() Contoso has a large database of customers and are looking to increase sales while retaining customers. Background processes can collate the IoT signals, predict maintenance requirements, and proactively schedule technicians.Ĭonsider a scenario where Contoso is a retailer with millions of existing customers. With elastic tables, you can import, store, and analyze large volumes of data without scalability, latency, or performance issues.Įlastic tables have unique capabilities for flexible schema, horizontal scaling, and automatic removal of data after a time-period.Įlastic tables automatically scale to ingest tens of millions of rows every hour. When to consider Dataverse elastic tables?Įlastic tables are designed to handle large volumes of data in real-time.
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