Real-Time Data Processing with Stream Analytics
Process millions of events per second and deliver instant insights that drive immediate business actions
Why Real-Time Stream Analytics?
Fraud Detection
Detect fraudulent transactions in milliseconds, blocking suspicious activity before damage occurs. Real-time risk scoring saves millions in fraud losses.
Personalized Recommendations
Update user recommendations instantly based on current behavior. Stream processing enables dynamic content personalization that boosts engagement.
IoT & Predictive Maintenance
Monitor sensor data from machinery, vehicles, or infrastructure. Predict failures before they happen and trigger automated maintenance workflows.
Real-Time Dashboards
Power live business dashboards showing current metrics. Monitor KPIs, application health, or user activity with sub-second latency.
Stream Processing Architecture
We build end-to-end streaming pipelines using industry-leading platforms and frameworks
Event Streaming Platform
Apache Kafka serves as the backbone of most streaming architectures. It provides durable, fault-tolerant message queues that decouple event producers from consumers. We deploy Kafka clusters optimized for high throughput and low latency.
Confluent Cloud
Fully managed Kafka with schema registry and enterprise features
AWS Kinesis
AWS-native streaming with tight integration to other services
Azure Event Hubs
Azure's managed streaming platform with Kafka compatibility
Google Pub/Sub
GCP's serverless messaging with global distribution
Stream Processing Engines
Processing engines consume event streams, perform transformations, aggregations, and enrichments, then output to downstream systems. We choose engines based on your latency, complexity, and scale requirements.
Apache Flink
The gold standard for complex event processing with exactly-once semantics, state management, and sub-second latency. Ideal for financial transactions, fraud detection, and real-time ML.
Spark Streaming
Micro-batch processing that unifies batch and streaming code. Good for teams familiar with Spark and use cases tolerating 1-5 second latency.
Kafka Streams
Lightweight Java library for stream processing, embedded in applications. Perfect for simple transformations and aggregations without separate infrastructure.
Real-Time Sinks & Storage
Processed events need to be stored or actioned. We integrate with databases, data warehouses, ML models, and operational systems for immediate value.
- Real-time databases: Redis, Cassandra, DynamoDB for low-latency reads
- Time-series databases: InfluxDB, TimescaleDB for metrics and IoT data
- Search engines: Elasticsearch for full-text search and analytics
- ML feature stores: Feast, Tecton for real-time feature serving
Ready to Process Data in Real-Time?
Our streaming experts will design an architecture that processes your event data with sub-second latency at any scale.
Stream Processing Concepts
Event Time vs Processing Time
Event time is when the event actually occurred (e.g., transaction timestamp). Processing time is when your system processes it. For accurate analytics, always use event time and handle late-arriving events with watermarks.
Example: A mobile app might buffer events offline and send them hours later. Processing time would be "now," but event time correctly reflects when the user action happened.
Windowing Strategies
Infinite streams need to be divided into finite chunks for aggregations. Common window types:
- Tumbling windows: Fixed-size, non-overlapping (e.g., count page views per 5-minute window)
- Sliding windows: Overlapping windows that slide (e.g., average transaction value over last 10 minutes, updated every minute)
- Session windows: Group events by activity periods separated by gaps (e.g., user browsing sessions with 30-minute timeout)
State Management
Stateful stream processing maintains context across events. Examples include running totals, recent history for anomaly detection, or user session data. Flink and Kafka Streams provide fault-tolerant state stores with automatic checkpointing.
Proper state management enables complex use cases like real-time fraud detection (maintaining user behavior profiles) or personalized recommendations (tracking recent interactions).
Exactly-Once Semantics
Distributed systems can fail at any point. Processing guarantees determine correctness:
- At-most-once: Fast but can lose data on failures
- At-least-once: No data loss but may process duplicates
- Exactly-once: Perfect correctness, each event processed exactly once (Flink, Kafka Streams support this)
For financial applications or ML training pipelines, exactly-once is critical to prevent duplicate charges or biased models.
Backpressure Handling
When downstream systems can't keep up with event velocity, backpressure mechanisms slow down producers gracefully. Without proper handling, systems crash or lose data. We design pipelines with monitoring and automatic scaling to handle traffic spikes.
Common Implementation Patterns
Lambda Architecture
Maintain both batch and streaming pipelines processing the same data. Batch layer provides accurate historical views, while streaming layer gives real-time approximations. A serving layer merges both for queries.
Use when: You need both real-time insights and batch accuracy, or when streaming infrastructure isn't mature enough for full historical reprocessing.
Kappa Architecture
Simplify by using only streaming pipelines. All data, including historical, is treated as a stream. Reprocessing happens by replaying the event log from the beginning.
Use when: Your streaming infrastructure is mature, event logs are retained long enough, and you want to avoid maintaining duplicate batch logic.
Event Sourcing
Store every state change as an immutable event. Current state is derived by replaying events. This provides complete audit history and enables time-travel queries.
Use when: Compliance requires complete audit trails, you need to reconstruct historical states, or when state changes are naturally event-driven.
CQRS (Command Query Responsibility Segregation)
Separate write models from read models. Stream processing transforms write events into optimized read views. Different views serve different query patterns.
Use when: Read and write patterns differ significantly, you need multiple optimized views of the same data, or when scaling reads independently from writes.
Real-World Success: E-Commerce Fraud Detection
The Challenge
A Nordic e-commerce platform was losing €2M annually to fraudulent transactions. Their batch fraud detection system ran nightly, meaning fraudulent orders shipped before detection. They needed real-time scoring at checkout.
Our Solution
We built a real-time fraud detection pipeline using Kafka and Flink. At checkout, transaction events are enriched with:
- Customer behavior from last 30 days (maintained in Flink state)
- Device fingerprint and geolocation data
- Real-time velocity checks (transactions per hour from same IP/card)
An ML model hosted in Seldon Core scores each transaction in under 100ms. High-risk transactions are flagged for manual review before fulfillment.
Frequently Asked Questions
When should I use stream processing vs batch processing?
Use stream processing when latency matters (seconds to minutes) and data arrives continuously. Examples: fraud detection, real-time dashboards, instant personalization. Use batch when hourly or daily updates suffice and you need to process large historical datasets efficiently. Many organizations use both: streaming for real-time features and batch for historical aggregations.
What's the difference between Kafka and Flink?
Kafka is a message broker that durably stores event streams and enables pub-sub patterns. Flink is a stream processing engine that consumes from Kafka (or other sources), performs transformations/aggregations, and outputs results. Think of Kafka as the "database" for events and Flink as the "compute" layer. Most streaming architectures use both together.
How do I handle schema evolution in streaming?
Use a schema registry (Confluent Schema Registry, AWS Glue Schema Registry) to version event schemas. Choose serialization formats that support evolution (Avro, Protobuf). Design consumers to be forward/backward compatible: add optional fields rather than changing existing ones, and handle missing fields gracefully. This enables producer and consumer upgrades without downtime.
What about streaming for machine learning?
Streaming enables real-time ML predictions by feeding live features to models. Feature stores (Feast, Tecton) serve pre-computed features with low latency. We also implement online learning pipelines that continuously update models with new data. Use cases include dynamic pricing, real-time recommendations, and adaptive fraud detection.
How much does a streaming infrastructure cost?
Costs vary by throughput and retention. Small deployments (1-10M events/day) on managed services cost $2,000-$10,000/month. Medium scale (100M-1B events/day) typically costs $20,000-$100,000/month. We optimize costs through appropriate retention policies, compression, and right-sizing compute resources based on actual throughput patterns.
Let's Build Your Real-Time Data Platform
Our streaming experts have built platforms processing billions of events daily. Let's discuss how real-time analytics can transform your business.