Data/IoT
Streaming data pipelines and real-time analytics systems that process high-velocity data and deliver actionable output in milliseconds, not minutes.
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Years in production
What it is
Real-time data solutions are systems that ingest, process, and act on data streams with sub-second latency — enabling live dashboards, instant fraud detection, dynamic pricing, and operational automation that batch processing and scheduled queries cannot support.
What you get
Batch processing is fine for reports that run overnight. It is not fine for fraud detection, live inventory, real-time bidding, or operational dashboards where stale data leads to wrong decisions. Real-time data architecture closes the gap between an event occurring and the system responding to it.
We build streaming pipelines on Apache Kafka, AWS Kinesis, and Google Pub/Sub — selecting the technology based on throughput requirements, latency targets, and your existing infrastructure. Stream processing with Apache Flink or Kafka Streams handles aggregations, enrichment, and anomaly detection in-flight, before data reaches storage.
The output layer matters as much as the pipeline. Real-time data is only useful if it reaches the right place: live dashboards via WebSocket or Server-Sent Events, materialised views in a database that front-end queries can hit without scanning the full event stream, or direct triggers to operational systems like alerting, fraud rules, or recommendation engines.
Key capabilities
Each engagement is scoped to your requirements — these are the core capabilities we bring to the table.
Change data capture (CDC) for database event streaming
Sub-second dashboard delivery via WebSocket and SSE
Anomaly detection and alerting in the stream layer
Schema registry and event schema governance
End-to-end latency monitoring with P99 SLA guarantees
Our process
A structured, engineering-led approach that moves from understanding your goals to a production system — with no handoff surprises.
Typical engagement
8–16 WEEKS
We map your goals, constraints, and existing infrastructure. Scope is defined and success criteria agreed before any development begins.
We design the technical approach, select the right tools, and produce a milestone-driven delivery plan with no ambiguity.
Iterative development with regular demos. Code reviews, test coverage, and documentation happen in parallel — not at the end.
Production release with monitoring setup and handover documentation. We stay close during the first weeks post-launch.
Built with
Real-time data processing analyzes and acts on data as it arrives, enabling instant insights and automated responses with sub-second latency using technologies like Apache Kafka and Redis.
Work with us
Share what you're building — we'll respond within one business day with questions or a proposal outline.
