Real-time data for the decisions that cannot wait until tomorrow
Real-time is worth building when a decision is made faster than your batch cycle: fraud declined at the transaction, stock shown as available at the moment of purchase, an operations alert while there is still time to act. It costs meaningfully more to build and to operate than a nightly batch, so the first question we ask is whether a fifteen-minute refresh would do — because frequently it would.
You probably need this if
- Batch means acting a day late
- Events arrive out of order
- A consumer failure loses the data
- A traffic spike collapses the pipeline
Where these projects go wrong
Four failure modes we see repeatedly. Knowing them up front is most of the battle.
- 01
Batch means acting a day late
Fraud is detected in the overnight run, by which point the goods have shipped and the chargeback is already in progress.
- 02
Events arrive out of order
The update lands before the create because two producers wrote to different partitions, and the consumer builds a state that never actually existed.
- 03
A consumer failure loses the data
The processor crashed for an hour, the events were not retained, and there is no replay path — so that hour is simply gone from the records.
- 04
A traffic spike collapses the pipeline
Producers outpace consumers during a sale, memory fills because nothing applies backpressure, and the whole processing chain stops rather than slowing down.
- Event schema registry with versioning
- Streaming ingestion with partitioning strategy
- Change data capture from operational databases
- Stateful processing with windowing and deduplication
- Exactly-once or idempotent consumer semantics
- Replay from retained events for reprocessing
- Live dashboards and threshold alerting
- Consumer lag monitoring with alerts
The engagement, step by step
- 01
Confirm real-time is actually required
We ask what decision changes with fresher data and how much sooner. If a fifteen-minute batch serves it, we will say so, because streaming is a permanent operational cost rather than a one-off build.
- 02
Define events and partition keys
A versioned schema per event and a partition key that keeps related events ordered. Ordering problems are near-impossible to fix once producers are live.
- 03
Build consumers that can be replayed
Idempotent processing with retained events, so a bug is fixed by reprocessing from the stream rather than by explaining a permanent gap in the data.
- 04
Test under spike load
Run at several times peak throughput before launch to verify backpressure and lag behaviour, since the failure you care about only appears under pressure.
Benefits & business outcomes
What changes when this work is done properly — not activity metrics, business results.
- 01
Decisions inside the event window
Fraud checks, live inventory and ops alerts fire while there is still time to act — not in tomorrow's batch when the goods have already shipped.
- 02
Ordering and replay designed in
Partition keys, retained events and idempotent consumers mean a crash becomes a reprocess, not a permanent hour missing from the record.
- 03
Backpressure instead of collapse
Spike load slows the pipeline rather than filling memory and stopping it — verified under several times peak before launch.
- 04
Honest latency targets
Sub-second or single-digit seconds agreed against measured volume — not architecture-diagram promises.
The stack for this work
- Apache Kafka
- Redis Streams
- Node.js
- TypeScript
- Python
- Debezium
- ClickHouse
- Grafana
- Payments and marketplaces needing live fraud checks
- Logistics and delivery operations tracking movement
- Products with live inventory or availability
- Teams monitoring operational thresholds continuously
Working remotely with teams in United States, United Kingdom, Canada, Europe, United Arab Emirates, Saudi Arabia, Singapore, Australia, New Zealand and India.
Industries we serve
The same engineering patterns apply across sectors — the domain language and constraints change.
- Payments & fintech
- Marketplaces & eCommerce
- Logistics & delivery
- IoT & fleet telemetry
- Gaming & live media
- Fraud & risk operations
- Ride-hailing & mobility
Why choose BuildsInfinity
Practical reasons teams choose BuildsInfinity for real-time data processing — grounded in how we actually work, not a slogan list.
- 01
We talk you out of streaming when batch wins
If a fifteen-minute refresh serves the decision, we say so. Real-time data processing is a permanent operational cost — worth it only when seconds matter.
- 02
Kafka only when scale demands it
Managed queues, Redis Streams or CDC often cover the need with less operational weight. We will not stand up a cluster for fashion.
- 03
CDC without rewriting the app
Debezium-style change data capture from the replication log makes existing databases emit events without triggers or application rewrites.
Related services
Data Engineering Services
Ingestion, modelling, orchestration and quality checks — pipelines that alert when they break rather than serving stale numbers.
ETL Development Services
Extraction, cleaning and loading between systems — idempotent, schema-drift aware, and reconciled against the source.
Data Warehouse Development
Dimensional modelling, history that survives source overwrites, incremental loads and query costs that stay predictable.
Need real-time data processing?
Send a paragraph about the problem and you will get a considered reply from an engineer — a shape, a rough timeline and an honest view on whether we are the right fit. Request a consultation or project estimate anytime.