Make data and AI useful
Act on events as they happen, not after the nightly load
We build streaming pipelines that ingest and process high-volume events, so people and systems can act on current information.
In short
Real-time data processing handles transactions, sensor readings and system events as a continuous stream rather than in batches. Events are validated, processed over event-time windows and served to dashboards, alerts and other systems. It suits workloads where timeliness matters, and a time-boxed pilot can test whether streaming earns its place.
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01 The problem
What problem does this solve?
Some decisions cannot wait for a daily batch. A payment needs checking, a machine is behaving oddly, a network is degrading. If the data arrives hours later, the response arrives hours later too.
Streaming brings its own difficulties. Events arrive late or out of order, formats change, and a stalled stream has to be noticed and recovered. It should be used where timeliness matters, not by default.
02 What we build
What do we build?
- Event ingestion from transactions, telemetry and database changes
- Schema validation and data contracts for every event type
- Stream processing with event-time windows
- An operational store and real-time dashboards
- Alerts and automated actions for downstream systems
- Monitoring, alerting and a recovery design for each stream
- A pilot that tests streaming against your real workload
03 Architecture
What does a typical architecture look like?
04 How it goes
How does the work usually go?
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Decide whether streaming is needed
We ask how fresh the data must be for the decision that depends on it, because batch or incremental loads can be simpler when timeliness is not critical.
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Prove it with a pilot
A time-boxed pilot tests streaming analytics on representative events, against success criteria agreed first. It ends in a decision to proceed, revise or stop.
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Design events and contracts
We define the event schemas and data contracts, the processing windows, and how late or duplicate events are handled.
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Build and test
We implement ingestion, processing and serving, and test reliability, failure recovery and performance under load.
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Operate with monitoring
We monitor data freshness, processing latency and recovery time, and review them as event volumes change.
05 Capabilities and stack
Which capabilities and technologies are involved?
- Apache Kafka
- Kafka Connect
- Apache Flink
- Spark Structured Streaming
- Apache Druid
- Apache Pinot
- Amazon Kinesis
- Azure Event Hubs
- Google Pub/Sub
The final stack follows your requirements and what you already run. How we choose technology
06 A typical situation
What does this usually look like?
An operations team follows equipment or transactions through reports that arrive after the fact, so problems are found when the working day is already over.
We stream the events from one source, process them continuously and show current status with alerts, tested first in a bounded pilot.
07 Questions
Questions people ask
Not always. Batch or incremental pipelines are simpler where nobody acts on the data straight away. We ask which decision depends on fresh data before recommending streaming.
Sources such as transactions, equipment telemetry, network events and meter readings, where timely processing supports operational intelligence. The test is whether a decision changes when the data arrives sooner.
Monitoring and recovery are designed for every stream, with alert thresholds and escalation paths agreed in advance. Recovery time is one of the measures we track against a baseline.
09 Terms
Which terms come up here?
If this sounds like you: Live data your product and your team can act on