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Ref: https://inferal.com/blog/databases-dont-know-why/
The provided text explores a fundamental limitation in modern database architecture, specifically how these systems operate in isolation without understanding the purpose of a query. Because traditional databases lack contextual awareness, they must treat every request as equally urgent, leading to inefficient resource allocation and unnecessary expenses. The author argues that this "not knowing" forces systems to maintain constant consistency and aggressive indexing even when the data is not immediately critical. To solve this, the source proposes a data-native system where business logic and data coexist to allow for smarter, outcome-based tradeoffs. By integrating the "why" behind a request, the system can prioritize essential tasks while deferring low-priority background work. Ultimately, this shift moves databases from being passive answer-engines to active participants in a larger business flow.
By KnowledgeDBRef: https://inferal.com/blog/databases-dont-know-why/
The provided text explores a fundamental limitation in modern database architecture, specifically how these systems operate in isolation without understanding the purpose of a query. Because traditional databases lack contextual awareness, they must treat every request as equally urgent, leading to inefficient resource allocation and unnecessary expenses. The author argues that this "not knowing" forces systems to maintain constant consistency and aggressive indexing even when the data is not immediately critical. To solve this, the source proposes a data-native system where business logic and data coexist to allow for smarter, outcome-based tradeoffs. By integrating the "why" behind a request, the system can prioritize essential tasks while deferring low-priority background work. Ultimately, this shift moves databases from being passive answer-engines to active participants in a larger business flow.