When data shows up faster than you can process it, batch pipelines fall behind and decisions get made on stale numbers. Events pile up, dashboards lag, and the system that was supposed to give you visibility becomes the bottleneck.
We build real-time data processing systems designed for throughput and durability. Kafka and NATS handle the firehose of events, Go workers process them with real concurrency, and TimescaleDB stores time-series data you can query instantly. The result is a pipeline that keeps up under load and doesn't lose a message when things spike.
For telemetry, transactions, clickstreams, or IoT data, we design for the volume you have now and the volume you'll have next year, with backpressure, retries, and exactly-once handling where it counts.
What you actually get.
Events ingested and processed per minute, proven under load before launch.
Durable queues, backpressure, and retries keep messages safe under spikes.
Time-series storage in TimescaleDB so you read results in real time, not overnight.
How we deliver it.
Model the event flow
We map where events originate, their volume and shape, and what has to happen to each one in real time.
Build the ingest layer
Kafka or NATS absorbs the firehose with durability and backpressure, so bursts don't take the system down.
Process with Go workers
Concurrent Go workers transform, enrich, and route events with the throughput interpreted runtimes can't match.
Store, observe, load test
We persist to TimescaleDB, add monitoring, and load test to prove the throughput targets before go-live.
The stack we use.
Proven tools, chosen for the outcome — not the resume.
Real builds, real results.
See how this work has played out for teams we've shipped for.
Questions teams ask before booking.
Both, depending on the job. Kafka is the workhorse for high-volume, durable, replayable event streams. NATS shines for low-latency messaging and lightweight pub/sub. We pick based on your throughput, latency, and durability requirements, not on what's trendy.
Durable, replicated queues, consumer acknowledgements, backpressure so producers slow down before anything overflows, and retries with dead-letter handling for failures. Where the business needs it, we implement exactly-once processing.
Go's goroutines handle massive concurrency with a small memory footprint, which is exactly what high-throughput stream processing needs. It sustains millions of events per minute at lower infrastructure cost than interpreted runtimes.
We design for your current peak and your projected growth, then load test to prove the target throughput before launch, so the numbers are verified on your infrastructure, not promised. We size that target together on the discovery call.
No, it complements it. We handle the real-time path so you act on fresh data immediately, while still feeding your warehouse for historical analytics.