Real-time Streaming
From batch to streaming-native.
We design streaming platforms that survive real traffic: bounded latencies, replay, exactly-once delivery and the observability to know what's actually happening at the topic level.
<200 ms
End-to-end event latency
1M+
Events per second throughput
Exactly-once
Delivery guarantee
Zero
Downtime during schema changes
What we ship
Capabilities
Kafka or Redpanda backbones with CDC from operational databases — sub-second pipelines that feed dashboards, fraud models and customer-facing experiences.
- 01
Kafka / Redpanda event backbone
High-throughput, durable event streaming — Kafka on managed cloud or Redpanda bare-metal for cost-sensitive deployments scaling past 1M events/sec.
- 02
Debezium CDC from Postgres / MySQL / Mongo
Capture every write from your operational databases in real time — replicated to the lakehouse and downstream consumers without polling or batch delays.
- 03
Flink or Spark Structured Streaming
Stateful stream processing for fraud scoring, sessionization and real-time aggregations — exactly-once semantics under failures and rebalances.
- 04
Exactly-once delivery & replay
Idempotent producers, transactional consumers and topic retention configured so any consumer can replay from any offset without duplicates.
Outcomes
- Sub-second data for dashboards and models
- Reliable CDC from your operational DBs
- Streaming foundations that scale past 1M events/sec
Tech we use
In the field
- 1
Mobile money fraud detection
Transaction events scored in under 200 ms — fraud model decision before the payment clears. 3× improvement in detection rate.
- 2
Real-time BI for regional retailer
CDC from 120 POS systems into a live Metabase dashboard — inventory and revenue visible as transactions happen.
- 3
Telco churn prediction
Usage events streamed into a feature store — churn model retrained nightly on fresh features, not stale batch snapshots.
How we deliver
Our delivery process
Every engagement follows the same rigorous four-stage approach — so you know exactly what to expect, and when.
- Step01
Traffic profiling
We profile your peak event rates, retention requirements and consumer SLAs before sizing any broker topology.
- Step02
CDC connector setup
Debezium connectors installed, tested and validated against your production database — slot lag and WAL retention tuned to your write volume.
- Step03
Stream processing design
Flink or Spark jobs designed with state backend sizing, watermarking and checkpointing to survive failures without data loss.
- Step04
Monitoring & alerting
Consumer lag dashboards, dead-letter topic alerting and broker health metrics — you know about a problem before your users do.
Ready to get started?
Build real-time streaming for your product
Tell us about your use case — we'll respond within one business day with a proposal scoped to your context.
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