Act on customer behavior the moment it happens
A streaming data fabric turns raw online and in-store signal into a continuously resolved identity and stable serving contracts — so AI decisions run on live truth, not last night’s batch.
Batch pipelines can’t feed real-time AI
Enterprise AI needs a real-time, trustworthy, auditable data foundation — not periodic ETL or a static warehouse. The fabric produces three canonical outputs: an Event Stream of continuous behavioral signal (not periodic batch), a Golden Record that continuously resolves identity (not one-time cleanup), and Serving Views that act as stable data contracts so upper layers stay insulated from storage changes. When decisions depend on hours-old data, intent has already expired.
A streaming fabric with stable serving contracts
Kafka is the event backbone, capturing every action as it happens. Flink runs real-time computation — windowing, state transitions and event-time semantics — while StarRocks provides high-performance analytical access. A Connector Catalog manages integration with POS, e-commerce, CRM, service, advertising and warehouse systems. Because the fabric follows a minimal-copy pattern, your existing warehouse stays authoritative; the loop only adds event processing, identity resolution and a serving layer on top.
How it works
The mechanics behind real-time data foundation.
Event Stream
Every online and in-store action lands on Kafka as a continuous behavioral signal. Flink computes over it with event-time semantics and windowing, so state reflects what a customer is doing now rather than what a nightly job last summarized.
Golden Record
Identity is resolved continuously, not in a one-time cleanup. As new events arrive, the record evolves — merging fragmented profiles into a single, current view that downstream semantics and agents can trust.
Serving Views
Upper layers read through stable data contracts. Storage engines and pipeline internals can change underneath without breaking the semantic, agent or application layers that depend on them.
Sub-100ms event processing at 50,000+ events/second, on a minimal-copy pattern — your existing warehouse stays the authoritative store while the fabric adds only event processing, identity resolution and a serving layer.
Frequently asked
Does this replace our data warehouse?
No. The fabric uses a minimal-copy pattern — your warehouse stays the authoritative store. Kafka, Flink and StarRocks add event processing, continuous identity resolution and a fast serving layer on top of it, rather than becoming a new system of record.
How does real-time here differ from a CDP’s batch sync?
A CDP typically consolidates data on a schedule. Here the Event Stream is continuous and Flink computes over it with event-time semantics, so state transitions and windows reflect behavior as it happens — the input AI decisions actually need.
How do upper layers stay stable if the storage changes?
Serving Views are explicit data contracts. Semantics, agents and applications read through those contracts, so the storage engine or pipeline internals can evolve without breaking anything above.
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