Data & AI Infrastructure Are Fusing

AI breaks the data stack.

Most enterprises spent the past decade building sophisticated data stacks. ETL pipelines move data into warehouses. Transformation layers clean data for analytics. BI tools surface insights to users.

This architecture worked for traditional analytics.

But AI demands something different. It needs continuous feedback loops. It requires real-time embeddings & context retrieval.

Consider a customer at an ATM withdrawing pocket money. The AI agent on their mobile app needs to know about that $40 transaction within seconds. Data accuracy & speed aren’t optional.

Netflix rebuilt their entire recommendation infrastructure to support real-time model updates¹. Stripe created unified pipelines where payment data flows into fraud models within milliseconds².

The modern AI stack requires a fundamentally different architecture. Data flows from diverse systems into vector databases, where embeddings & high-dimensional data live alongside traditional structured data. Context databases store the institutional knowledge that informs AI decisions.

AI systems consume this data, then enter experimentation loops. GEPA & DSPy enable evolutionary optimization across multiple quality dimensions. Evaluations measure performance. Reinforcement learning trains agents to navigate complex enterprise environments.

Underpinning everything is an observability layer. The entire system needs accurate data & fast. That’s why data observability will also fuse with AI observability to provide data engineers & AI engineers end-to-end understanding of the health of their pipelines.

Data & AI infrastructure aren’t converging. They’ve already fused.

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