Real-time data infrastructure enables your systems to react to events as they occur: new orders, inventory changes, user actions, sensor readings, and system alerts. We build streaming pipelines, event-driven architectures, and the alerting that turns events into action.
Most reporting is fine on a nightly schedule. Real-time earns its complexity when the value of a signal decays in minutes: fraud, stock-outs, payment failures, SLA breaches, and live operations.
An order stuck in a failed payment state is recoverable in the first minutes and lost by tomorrow. A stock-out during a campaign burns ad spend every second it goes unnoticed. A failing integration silently drops records until someone checks a report the next morning.
For these, the batch answer arrives after the decision window has closed. Streaming moves the detection to the moment the event occurs, and pairs it with an automated or human response.
A simulated live feed in the shape we deploy: typed events, payload snippets, and the latency stats that tell you the stream is healthy. Hover to pause.
Pub/Sub, Kafka, and managed streaming services that handle millions of events per day. Durable, ordered, and exactly-once delivery where it matters.
Ingest webhooks from SaaS platforms, payment processors, shipping providers, and any system that pushes events. Buffered, validated, and routed to the right destination.
Transform and load data as it arrives, not in nightly batches. Real-time enrichment, filtering, aggregation, and delivery to your warehouse or operational systems.
Dashboards that update in real time: order flow, system health, inventory levels, and operational KPIs. Your team sees the current state, not yesterday's snapshot.
Event-driven alerts and automated responses: stock threshold breaches, payment failures, system anomalies, and SLA violations detected and acted on immediately.
CDC pipelines that capture every insert, update, and delete from your operational databases and stream them to your warehouse or downstream systems in near-real-time.
We recommend streaming where the decision window demands it and batch where it does not. Over-engineering everything to real-time is how cloud bills explode.
| Question | Batch is right when | Streaming is right when |
|---|---|---|
| Decision window | Decisions are daily or weekly: finance, strategy, most reporting | Value decays in minutes: fraud, stock, payments, live ops |
| Data shape | Large periodic snapshots, historical analysis | Discrete events: orders, clicks, sensor readings, state changes |
| Cost profile | Cheapest per record, compute runs briefly on schedule | Always-on but lean when engineered for the actual volume |
| Failure mode | A late batch, rerun in minutes | Requires dead-letter queues, replay, and idempotent consumers, which we build in |
| Our recommendation | Default for reporting workloads | Deployed surgically on the events that justify it |
The strongest platforms are hybrid: batch for depth, streaming for reflexes. We design both lanes into one coherent architecture.
Not when scoped honestly. A webhook-plus-Pub/Sub lane sized to your actual event volume is lean; costs explode only when everything is forced through streaming by default. We deploy it surgically.
Events are durable: they queue, and dead-letter handling catches poison messages. When the consumer recovers, it replays what it missed. Losing events is a design failure we engineer out.
Yes. Streaming ingestion and CDC land events in the same warehouse your batch pipelines use, giving analysts minute-fresh tables without a separate stack.
Pick the single signal with the highest cost of delay, typically payment failures or stock thresholds, and put one streaming lane plus one alert on it. Expand from proven value.
Tell us which signal hurts most when it arrives late. We will design the streaming lane that fixes it.