C.4 · Data Engineering & Analytics
Real-Time & Streaming Analytics
Sub-second freshness where a stale number is a wrong decision
What we build
Streaming architectures for the decisions that cannot wait for tonight's batch. Event ingestion at scale over Kafka or Pulsar, stream processing in Flink or Spark with exactly-once sinks, windowing and late-arrival handling that reflects how the business actually thinks about time, real-time aggregation into serving stores, and operational dashboards that update without a refresh button.
Capabilities
- Event ingestion at scale with ordering and partitioning designed for the query pattern
- Stream processing with exactly-once sinks where correctness demands it
- Windowing and late-arrival handling that matches how the business defines a period
- Real-time serving stores for sub-second lookup on aggregated state
- Alerting and anomaly detection on the stream rather than on yesterday's table
- Backfill from the same code path, so streaming and historical results agree
Related services
How it connects
Where it sits in the stack.
This service, and the two it hands off to. None of them can be optimised alone.
Streaming Analytics
Streaming architectures for the decisions that cannot wait for tonight's batch.
Data Platforms
Data platforms built for freshness and lineage.
Data Engineering & Analytics · see serviceBusiness Intelligence
Reporting that a leadership team can actually argue from.
Data Engineering & Analytics · see serviceBring us the whole problem.
Tell us where the work is stuck, whether that is a model that never reached production, an application nobody can change, a data platform nobody trusts, or a plant the business cannot see. An engineer replies with a first read, not a sales deck.