Introduction to Stream Processing Architectures
A comprehensive overview of stream processing patterns, including lambda and kappa architectures, and how modern systems handle unbounded data streams with exactly-once semantics.
Showing 1–9 of 1,240 articles in Real-Time Processing
Apache Kafka vs. Apache Pulsar: A Deep Comparison
Analyzing the design philosophies, performance characteristics, and operational considerations of two leading distributed streaming platforms for enterprise deployments.
Real-Time Fraud Detection Systems
How financial institutions leverage sub-millisecond processing pipelines, machine learning inference, and complex event processing to detect fraudulent transactions as they occur.
Windowing Techniques in Stream Computing
Understanding tumbling, sliding, and session windows, and how to choose the right aggregation strategy for time-based stream processing applications.
Real-Time AI Inference at the Edge
Deploying lightweight models to edge devices for instant inference, reducing latency and bandwidth requirements in IoT and autonomous systems.
State Management in Distributed Stream Processors
Best practices for managing state in stateful stream processing applications, including checkpointing, snapshots, and fault tolerance mechanisms.
Apache Flink vs. Spark Streaming
Comparing micro-batch processing with true stream processing, latency benchmarks, and when to choose Flink over Spark for real-time workloads.
Building Real-Time Recommendation Engines
Architecting recommendation systems that update scores instantly based on user behavior, combining stream processing with feature stores and model serving.
Optimizing Latency in Distributed Systems
Techniques for reducing tail latency, including connection pooling, batch size tuning, network optimization, and garbage collection strategies for low-latency services.