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The motivation behind CDC | solution-architecture-patterns

chanakaudaya.github.io · 1,506 words · saved by 1 readers

Applications start with a small data footprint. Initially, a single database fulfils every data need of the application. When applications evolve, they need to support different data models and data access patterns. For example, they might need a search index to perform full-text searches, a cache to speed up the reads, and a data warehouse for complex analytics on data. Eventually, that simple architecture evolves into something like this. ![](./images/cdc-intro.png “”) Practically speaking, no one database can satisfy all those needs simultaneously. Consequently, applications have to use different data storage technologies such as indexes, caches, and warehouses together in their architecture. That forces them to keep their data in multiple places, in a redundant and denormalised manner. When you have multiple versions of the same data set, you need to appoint one as the source of truth, or the authoritative version. That means, when there’s a discrepancy across versions, the source

The motivation behind CDC | solution-architecture-patterns Skip to the content. The motivation behind CDC Applications start with a small data footprint. Initially, a single database fulfils every data need of the application. When applications evolve, they need to support different data models and data access patterns. For example, they might need a search index to perform full-text searches, a cache to speed up the reads, and a data warehouse for complex analytics on data. Eventually, that simple architecture evolves into something like this. ![](./images/cdc-intro.png “”) Practically speaki

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