Real-Time Big Data Processing

Real-time processing analyzes data continuously as it streams into your systems, rather than collecting it first and processing in batches. This paradigm shift enables immediate insights and instant reactions to events.

Traditional batch processing-running nightly or hourly jobs-creates latency between events and insights. For many use cases, this delay is unacceptable. Fraud must be detected in milliseconds, not hours. IoT sensors need immediate anomaly detection. Customers expect real-time personalization.

Our real-time processing solutions use stream processing frameworks like Kafka, Spark Streaming, and Flink to handle continuous data flows. We design for the unique challenges of streaming: handling late-arriving data, maintaining state across events, ensuring exactly-once processing, and scaling to handle traffic spikes.

We have built over 50 real-time processing pipelines handling millions of events per second across industries including financial services, e-commerce, IoT, and telecommunications.

Real-time systems have unique operational challenges. They run continuously, require careful state management, must handle failures gracefully, and need to scale dynamically with traffic. Our team has deep experience addressing these challenges-we have operated streaming systems processing billions of events daily.

We understand the tradeoffs between different streaming technologies. Kafka for reliable event transport, Flink for complex stateful processing, Spark Streaming for unified batch and stream, managed services for operational simplicity. We help you choose the right tools for your specific latency, throughput, and complexity requirements.