![]() ![]() Pre-registration checks: The hub Schema Registry ensures that if two schemas that are attempted to be registered are equal, the same Schema ID is assigned to both of them.A single source of truth: All schema registration happens at the “hub,” so there is no fear of overlapping schema IDs or subject naming conflicts as Schema Registry provides an ever-increasing ID for new schemas and prevents subject collision.The data sharing component involves only replicating schemas that are needed (or all) by the “spokes,” or individual business units that wish to consume by utilizing a specific data contract. The “hub to spoke” pattern involves utilizing a single Schema Registry globally in an organization. There are multiple ways to tackle these guarantees, and we will discuss them in the following patterns: Ensuring the schema definitions are unique.Internally, these monotonically increasing IDs are assigned by the Schema Registry and, by default, start from “0.” Allowing these contracts to coexist in multiple Schema Registry deployments involves: This situation also arises when multiple business entities with their own Confluent deployments merge.Ĭonfluent’s Schema Registry uniquely identifies a schema for an Apache Kafka ® record through the use of “schema IDs.” These IDs are individually included in every message so that consumers such as Kafka clients, connectors, and ksqlDB queries understand what schema to reference in order to make sense of the data being consumed. Sharing these contracts with the business as a whole is then paramount for the success of the data product. To isolate for their own data needs, teams commonly deploy multiple Schema Registry clusters to serve their own contracts. Schema Registry ensures a proper data contract between the creators and consumers of this product however, not all data products from a business unit are necessarily needed by all possible consumers of the organization as a whole. ![]() This evolution is commonly referred to as the “ data mesh.”Ĭonfluent’s Schema Registry allows organizations to ensure high data quality and safe data evolution as they set their data in motion and operationalize the data mesh. As the usage of real-time data grows, it is a common evolutionary practice to enable servicing this data as a product within-and outside-the business. ![]() Sharing metadata on the data you store in your Confluent cluster is paramount to allow for effective sharing of that data across the enterprise. ![]()
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