Streaming use case
Decision timing and service-level thresholds before any infrastructure choice.
Data engineering
Streaming analytics for event-driven operations and automated decisions
Event pipelines, CDC patterns, live metrics, anomaly alerts, and automated decision support for teams where late data changes business outcomes.
Inspectable delivery artifact
A buyer should see the operating evidence, not only read the promise.
This service is evaluated through source contracts, quality checks, owners, failure states, and handover evidence. The preview shows the kind of artifact that belongs in the delivery packet.
Streaming analytics for event-driven operations and automated decisions
Sample acceptance matrix
source_contract
Check
owner + grain
State
pass
Owner
Data eng
pipeline_run
Check
retry + replay
State
pass
Owner
Platform
dashboard_view
Check
certified mart
State
review
Owner
Business
| Artifact | Check | State | Owner |
|---|---|---|---|
| source_contract | owner + grain | pass | Data eng |
| pipeline_run | retry + replay | pass | Platform |
| dashboard_view | certified mart | review | Business |
Pipeline integration
How events land into queryable storage and analytical serving paths.
Live outcomes
Operational views that surface freshness, lag, anomalies, and owner routing.
Where this fits
Use this service when a business process already depends on data, but the current path from source system to accepted metric is fragile, undocumented, or too dependent on one person.
DataDost AI provides event pipelines, streaming architecture, CDC, live metrics, anomaly alerts, and automated decision support for startups and growing companies.
What we build
▾
We define event catalog, stream architecture, replay strategy, failure behavior, lag monitoring, and downstream serving model for each use case.
Streaming use-case assessmentEvent catalogReference architectureCDC or stream designSchema and compatibility rulesLag monitoringReplay procedureDead-letter handling
Engineering decisions we make explicit
▾
Event schemas, ordering expectations, idempotency strategy, backpressure handling, retry strategy, and checkpoint/replay model are set in writing.
Controls and quality checks
▾
Controls include lag alerts, event volume anomaly checks, late-arrival handling, duplicate event protection, and consumer failure diagnostics.
Streaming need justified by business timingEvents have schemasLate and duplicate events are handledLag thresholds are definedFailures create alertsReplay is possibleConsumers are documentedFallback reporting exists
How we work with client data
▾
Where possible, we begin with read-only or sample-driven proofs before opening persistent real-time capture rights.
Pilot-first access patternLeast privilege and rotation policyRetention and replay policies defined by business valueDownstream owners and alert route defined by function
What handover looks like
▾
Handover includes event catalog, stream design, consumer list, lag thresholds, alert routes, replay and incident runbooks.
Architecture summary with assumptions and trade-offsSource inventory and ownership notesRunbook for routine operation and failure handlingKnown limitations, backlog, and scale-up recommendationsUser-facing explanation for business stakeholders
How we demonstrate this service
▾
We use one real-time decision use case and prove behavior from event arrival through operator response, including exception handling.
End-to-end topology: producers, stream bus, processors, and consumersNormal event flow and lag profile across decision windowsInjected duplicate and late-event recovery with dedupe/replay proofDead-letter queue inspection and manual recovery pathFallback batch reporting when live streams exceed tolerance windows
What you can review in the demo
▾
Each event-driven outcome is paired with owner action so decision ownership is measurable.
Live lag and threshold-crossing panelReplay execution result with verification outcomeDuplicate/lateness simulation logsIncident timeline with assigned owners and actionsAlert routing matrix for on-call and finance operations
Why this matters▾
Streaming is valuable only when delay has business cost. When decisions must happen in minutes or seconds, batch is the bottleneck.
Security and data handling
Access is role-based, production changes are documented with rollback notes, and handover includes access assumptions and evidence.
Working surface
Map the first reliable data path.
Bring the source exports, dashboards, and decisions that currently create friction. We return a scoped pilot path with owners, evidence, and handover expectations.