Streaming Architecture Design
We design event streaming for your latency and scale needs.
Real-Time Data Streaming Services
Real-Time Data Streaming Services
Trusted across 20+ countries by Fortune 500 companies and growth-stage brands
We build streaming data systems that process events the moment they happen, so you can detect, decide and act in real time instead of waiting for the next batch. Over a decade of experience, 250+ digital solutions delivered.
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What is real-time data streaming?
Real-time data streaming is the continuous processing of data as it is generated, rather than in scheduled batches. It powers use cases where seconds matter, such as fraud detection, live dashboards, personalisation and monitoring. Noseberry builds streaming pipelines on Apache Kafka and Spark Streaming that ingest, process and route events reliably, feeding real-time analytics and AI.
Key takeaways
- Streaming processes data continuously, as events happen, not in batches.
- It powers real-time use cases like fraud detection, monitoring and personalisation.
- Apache Kafka and Spark Streaming are the core technologies.
- Reliability and low latency are what make streaming production-grade.
2M+Lives touched
15+Fortune 500 clients
20+Countries served
250+Digital solutions delivered
What we do
Our real-time streaming services
Kafka and Event Pipelines
We build and operate Kafka-based streaming pipelines.
Stream Processing
We process, enrich and transform data in flight with Spark Streaming and Flink.
Real-Time Analytics
We feed live dashboards and alerting from streams.
Streaming to AI
We route real-time data into models for instant inference.
Reliability Engineering
We build for exactly-once processing, monitoring and failover.
Where it delivers value
Where real-time streaming delivers value
Fraud and anomaly detection as it happens
Live operational dashboards and alerting
Real-time personalisation and recommendations
IoT and sensor data processing
Event-driven microservices and automation
How we work
Our five-phase process
We prove a streaming use case end to end before scaling to production volumes.
1
Discovery and Audit
We map your events, latency needs and destinations.
2
Strategy and Roadmap
We design the streaming architecture and plan.
3
Rapid Proof of Concept
We prove a streaming use case end to end.
4
Build and Integrate
We build the pipelines and connect analytics and AI.
5
Deploy and Optimize
We scale to production volumes with monitoring and failover.
Technology we use
Streaming
- Apache Kafka
- Spark Streaming
- Apache Flink
Storage & sinks
- Snowflake
- Databricks
- Cloud data lakes
Orchestration & ops
- Kubernetes
- Monitoring & alerting tools
Cloud
- AWS
- Azure
- Google Cloud
Security and compliance
Secure streams, end to end
Streaming systems are built with encryption in transit, access controls and monitoring. We build to GDPR, HIPAA and SOC 2, deployed on AWS, Google Cloud and Azure.
GDPRHIPAASOC 2
Real success storiesFinTech · Digital Insurer PropTech · Operations E-Commerce · Retail leader
Outcomes we have driven
Challenge
Fraud decisions were delayed by batch data.
Solution
A Kafka streaming pipeline scoring claims the moment they arrive.
Impact
93% of fraud caught pre-payout, ~$4.2M saved annually.
Challenge
Operational data lagged behind real events across markets.
Solution
Real-time streams feeding live dashboards and alerting.
Impact
40% faster response and valuations.
Challenge
Personalization ran on stale, batch data.
Solution
Streaming events into the recommendation engine for live scoring.
Impact
+28% lift in conversion rate.
Sector-anonymised outcomes shown until named clients are approved.
Why Noseberry
Why choose Noseberry for real-time streaming
Specialist
AI, Cloud and Data is our core, no generalist dilution.
Reliability-first
Exactly-once processing, monitoring and failover built in.
AI-connected
We route real-time data straight into models.
Proven at scale
250+ solutions delivered across 20+ countries.
Real-time streaming, answered.
Batch processes data on a schedule, streaming processes it continuously as events occur. Streaming is used when decisions cannot wait for the next batch.
When seconds matter, such as fraud detection, live monitoring, personalisation or IoT. If daily or hourly data is fine, batch is simpler and cheaper.
Primarily Apache Kafka for event streaming and Spark Streaming or Flink for processing, on your cloud.
Yes. We route real-time data into models for instant inference, enabling live scoring and decisions.
Through exactly-once processing, durable event storage, monitoring and failover, so events are not dropped or double-counted.
Need to act on data in real time?
Book your free 30-minute strategy session and we will scope a streaming solution.
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