Understand behaviour
while it can still change the outcome.
Turn live interactions from web, app, transactions, service and operations into current, governed intelligence. Datastreams keeps collection, meaning, policy and delivery in one managed operation instead of another multi-service implementation programme.
From interaction to intelligence
One live understanding across customer and operational behaviour
Capture what happened, connect it to current context and make the permitted insight or action available while it remains useful.
Every interaction, captured with meaning
Clicks, orders, sensor readings, app events, back-office actions. Captured once, described in business language, not in a tracking plan only engineers can read.
Validation and governance while it moves
Structure, quality, consent, purpose and identity can be checked at the moment of the event, so invalid data is rejected, repaired or routed through an explicit exception path.
Real-time context for agents
Your AI agents and apps can read the same live, permitted context that people do, within the deployment and provider boundaries you choose.
Declare destinations behind a contract
Warehouse, lake, stream, app, inbox or agent. Compatible destinations reuse the stream definition and follow the governed adapter lifecycle.
The difference
A chain of provider services, or one accountable runtime boundary
A cloud event platform is commonly composed from collection, messaging, processing, storage and observability services. Datastreams reduces the intermediate services the customer must assemble and keeps responsibility for the managed runtime explicit.
The classic event stack
- A collector service to deploy and scale
- A message queue to size, monitor and pay for
- An enrichment service with its own release cycle
- A schema registry as a separate moving part
- Loaders per destination, each one a failure point
- A warehouse job before anything is usable
- Reverse pipelines to push data back into operations
- A data team responsible for keeping the complete chain aligned
Datastreams runtime
- One runtime service instead of a customer-managed event stack
- Streams handled in memory, no queue to babysit
- Enrichment is a recipe line, changed at runtime
- Meaning lives with the data, not in a side registry
- Destinations are declared, not engineered
- Data is usable at the event, not after the load
- Operations act on the same stream, no round trip
- Fewer intermediate event services for the customer to maintain
Personal data without platform sprawl
More obligations, the same stream configuration
Identity, consent, purpose and evidence make the business operation more responsible. They should not force the customer to assemble another data platform.
A simple event
source → contract → destination
Enough when the operation only needs validated delivery.
A personal-data event
source → contract → identity → consent → purpose → action → evidence
The definition becomes richer, while the runtime, lifecycle and operator interface remain the same.
Why one runtime is enough
Keep the operation close to the data in motion.
Every handover between services can add data movement, delay and another place where context must be preserved. Datastreams keeps the core stream operation inside one runtime boundary. Capture, meaning, control and delivery happen inside one execution boundary sized for the agreed workload.
Datastreams carries the agreed sizing, patching and runtime service responsibility. Approved agents read the same governed context and are held to the same permitted actions as people.
Compare it with your stackFewer services in your way
Reduce the collectors, brokers, workers and handovers your organisation must operate.
Computation where the data is
Heavy validation, enrichment and matching happen in flight, not in a later batch job.
Capacity follows the workload
Size the serviced runtime against representative event volume, latency and processing needs.
Job by job
Bring the core behavioural-data jobs into one operating model
Tracking, schemas, enrichment, identity, consent, delivery and real-time attributes remain separately understandable without becoming a separate customer-managed service chain.
Tracking and SDKs
Dozens of trackers per channel, each with its own release cycle and tracking plan.
One capture endpoint for web, app, server, device and back office. The definition lives in the recipe, so a change ships at runtime.
Schemas and validation
A separate schema registry, versioned outside the pipeline, with failed-event queues to reprocess.
Meaning travels with the event. Structure, types and business rules are checked in flight, and rejected events are explained where they happen.
Enrichment
A stream processing app per enrichment, deployed and scaled by your team.
Enrichment is a declarative line: lookups, geo, device, campaign, currency, product context. No service to deploy.
Identity
Stitching jobs in the warehouse, hours after the visit, with cookies as the anchor.
Identity is resolved while the session is alive, so the next action already knows who it is talking to.
Consent and purpose
Consent stored elsewhere and applied late, if it is applied at all.
Purpose and permission are enforced at the event, before anything is stored, shared or used by an agent.
Delivery
A loader per warehouse, lake and stream, plus reverse pipelines to get data back into operations.
Declare the destination. The same governed stream serves warehouse, lake, app, inbox and agent at once.
Real-time attributes
A separate real-time intelligence product on top of the pipeline, priced per profile.
Counters, session state and derived attributes are computed in the stream and readable immediately.
Operating model
Fully managed SaaS, private cloud or self-hosted, each with a different bill and a data team behind it.
One runtime where your data must live, with the same behaviour and an agreed workload and service model.
Ownership and economics
Behavioural data only creates advantage while you still own it
Per-event pricing, vendor-held copies and a permanent integration burden are commercial and architectural choices. Make them visible before accepting them.
A visible workload model
Evaluate the real stream: volume, processing, state, data movement, deployment and service responsibility. Cost should not be reconstructed from disconnected product invoices.
Your data never becomes their asset
Raw and enriched data remains inside the agreed deployment and retention boundaries. Portability and recovery requirements are made explicit before the service is operated.
Decisions belong in the moment
If behavioural context arrives after the campaign, the match or the checkout, it can no longer influence that interaction. The operation must act while the event is moving.
Transparency instead of a black box
Every rule, enrichment and permitted action is readable and runtime auditable, by your team and by your auditors.
The business case
Fewer moving parts is where the money and the delay disappear
Runtime instead of eight services to operate
Context and policy applied before delivery
Intermediate event services to operate and monitor
Change a source or consumer through the runtime definition
Where it pays off
What teams do the day the delay is gone
Make data location, processing and provider responsibility explicit.
The deployment records what is collected, where it lives and which rules apply. Runtime evidence makes configured actions inspectable, while portability and recovery requirements are agreed and tested for the selected infrastructure.
Questions we get
Straight answers before you rebuild anything
Is this a replacement for an event platform like a behavioural data pipeline?
It can replace those event-platform jobs where the required sources, contracts and destinations are supported. The assessment maps collection, validation, enrichment, identity, consent, attributes and delivery before any component is retired.
What happens to our warehouse and lake?
They stay. They simply stop being the place where data first becomes usable. The stream is already governed and enriched, so the warehouse becomes one destination among several, not the bottleneck.
How is this different from a wrapper around big-tech cloud?
A composed cloud architecture meters and operates several building blocks separately. Datastreams executes capture, meaning, control and delivery inside one serviced runtime, while any required external provider remains an explicit adapter and cost.
Can our AI agents use this directly?
That is the point. Agents read the same live, permitted context as people and are bound by the same rules, so automation never gets a wider mandate than your policy allows.
How long does it take to see the first stream running?
The timing depends on the source, contract and deployment. There is no complete event-platform stack to assemble first: we begin with the stream definition and required outcome.