DATA STREAMING AND REAL-TIME ANALYTICS
Real-time data streaming and analytics
We build data streaming and real-time analytics for time-sensitive decisions, with event processing, fresh serving layers and controls for late or duplicated data.
- Exactly-once processing, so results are correct, not just fast
- Event-time windows and watermarks that handle late and out-of-order data
- Streaming and batch on one governed foundation, so there is a single truth
Streaming is more demanding than batch, and not every workload needs it.
The honest question is how fast the value of a decision decays. We do not push streaming where batch is sufficient. We use it where a decision loses its value before the next batch would run, and often run both against a shared architecture so historical and real-time views stay consistent. This is the real-time track of Data and Analytics Services, built on the same foundation as Data Engineering.
We qualify each use case honestly before building, and often run streaming and batch against a shared architecture so there is one version of the truth, not two.
Act while it still matters, not after the batch runs.
Data streaming and real-time analytics, from events to action.
- An event backbone on Kafka or a managed equivalent: durable, ordered, and replayable.
- Stream processing jobs in Flink or Spark Structured Streaming for transforms, joins, and detection.
- A real-time serving layer, so applications and dashboards read fresh state with low latency.
- Exactly-once and late-data handling, so results are correct, not just fast.
- Monitoring and alerting on lag, throughput, and processing health.
- Replay from the event log when logic changes, so you can rebuild state without data loss.
Qualify, build and test under load.
Qualify
Does the decision's value decay fast enough to justify streaming, or is batch sufficient?
Design
The event model, topics, and processing topology, sized for throughput and correctness.
Build
Ingestion, processing, and serving with correctness checks and recovery controls built in from the start.
Validate
Test against replay and late-data scenarios before go-live, so surprises surface early.
Operate
Monitor lag and health, tune for cost and throughput, or hand over to your team.
An event pipeline, engineered for correctness under continuous load.
A representative architecture by layer. We work with open tools and managed equivalents, choosing by control, portability, and operational simplicity.
We engineer the hard parts that make streaming trustworthy: exactly-once processing, event-time windows and watermarks for late data, backpressure handling, and replay from the event log. See our Data Platforms practice for the foundation it runs on.
What real-time feeds, and what it is fed by.
Data Engineering
The medallion foundation streaming shares, so batch and real-time stay consistent.
Data Management and Governance
The governance layer that keeps real-time and historical views trustworthy.
Fraud Detection
Real-time scoring that blocks fraud inside the decision window, not after it.
Data Democratization
Governed self-service, so live insight reaches the people who act on it.
Data and Analytics Services
The parent practice this real-time track belongs to.
Data and Analytics
The wider capability that turns data into decisions across the business.
What data leaders ask us first.
If the decision loses value within minutes, streaming has a stronger business case. If not, micro-batch or scheduled batch is cheaper and simpler. We qualify each use case before building.
Both are valid. We use open Kafka and Flink where control and portability matter, and managed equivalents where operational simplicity wins. The architecture is the same.
With event-time processing, windows, and watermarks, so a late event still lands in the right window. We validate this with replay before go-live.
Build the streaming foundation that lets your business decide in seconds, not overnight.
A durable event backbone, correct-by-design processing, and low-latency serving, qualified honestly against your use case.