Latency in seconds
Fresh signals for operations, fraud, logistics and pricing while they can still change the outcome.
Data Analytics
Real-time analytics
Streaming pipelines and operational dashboards for the decisions that cannot wait for tomorrow's batch.
What we do
Some decisions expire in minutes: fraud checks, stock allocation, pricing, incident response. Real-time analytics moves those signals from tomorrow's report into a live pipeline, using Kafka, change data capture and stream processing to bring latency from hours down to seconds. We build that path on your cloud, next to the warehouse you already have.
We are deliberately conservative about what needs streaming, because real-time infrastructure costs more to run and operate. The engagement starts by pricing the latency requirement per use case; whatever genuinely needs seconds gets a streaming path, the rest stays on cheaper batch. You see that split, with running costs, before we build anything.
In production this looks like operational dashboards that update live, alerts firing on thresholds and events flowing back into your systems to trigger action. Your batch warehouse and the streaming path share definitions, so the live number and tomorrow's report finally agree. Your team gets runbooks, replay procedures and monitoring, so operating it is routine.
Why it pays off
Streaming only where it pays
Each use case gets the cheapest latency that meets the need. Batch stays batch.
One set of definitions
Streaming metrics reuse the warehouse's governed definitions, so live and daily numbers match.
Alerts, not just charts
Thresholds and anomalies notify the responsible person or trigger an automated response.
Operable by your team
Monitoring, replay and dead-letter handling built in, so incidents are routine, not heroics.
Data Analytics
How we work
01
Latency audit
We list candidate use cases and price the value of seconds versus hours for each of them, honestly.
02
Streaming backbone
Kafka or managed equivalents plus CDC from your operational databases, deployed as code.
03
First live use case
One high-value flow goes end to end: events in, processed metrics out, dashboard and alerts live.
04
Scale and operate
More flows onboard onto the same backbone, with runbooks and on-call handover to your team.
Deliverables
Latency requirements map per use case
Streaming backbone with CDC ingestion
Live operational dashboards and alerting
Reconciliation with batch warehouse numbers
Runbooks for replay and incident response
Questions, answered
Do we actually need real-time?
Often only for a slice of your analytics. We would rather tell you that batch is enough than sell you a streaming platform, and the latency audit gives an answer per use case in the first weeks.
What does streaming infrastructure cost to run?
More than batch, which is exactly why we scope it narrowly. Managed services like MSK, Pub/Sub or Confluent keep the operational load reasonable, and the architecture document puts monthly figures on your workload.
Can it feed actions, not just dashboards?
Yes. Processed events can flow back into your systems through APIs and webhooks: blocking a transaction, replenishing stock or paging a person. Analytics that triggers action is usually where real-time pays off.
Find out what real-time is worth
Bring your use case and get a straight answer on latency, architecture and running cost.
Book a discovery call