From Hours to Milliseconds

Most enterprise data pipelines still rely on batch ETL — extract overnight, transform in the morning, load by lunch. For internal dashboards that cadence is fine. But the moment that same data powers a customer-facing experience, the gap between “fresh enough” and “stale” becomes the gap between conversion and abandonment.

The latency spectrum runs from hours (classic batch) through minutes (micro-batch) to true milliseconds (stream processing). Each step down that spectrum unlocks new categories of experience: real-time recommendations, fraud detection before a transaction settles, pricing that reflects the market right now rather than the market at last night’s close.

The shift from batch to live is not about replacing your data warehouse. It’s about adding an operational data plane that sits between your systems of record and your systems of engagement. MongoDB Atlas, combined with Atlas Stream Processing, acts as that plane — ingesting change streams, applying windowed transformations, and materializing results into collections that your application reads with single-digit-millisecond latency.

Three patterns emerged repeatedly across the use cases we explored in Stockholm. Each one is independently valuable and can be adopted incrementally — you don’t need to rebuild your stack to start seeing results.

Every serious AI deployment eventually hits the same wall: the model is only as good as the data it can access at inference time. Retrieval-augmented generation, real-time scoring, and agentic workflows all demand a data plane that returns results in milliseconds, not minutes.

The live data plane we built throughout this talk is exactly that foundation. When your operational database already serves fresh, pre-computed features alongside vector embeddings, adding an AI layer becomes an incremental step rather than a separate infrastructure project.

The practical advice: start small.