Streaming Architectures

Streaming architectures are software design patterns and systems that process continuous, real-time data streams as they are generated, rather than in batches. They enable low-latency data ingestion, processing, and analysis for applications like live analytics, event-driven systems, and IoT data pipelines. Key components often include message brokers, stream processors, and storage layers optimized for sequential data access.

Also known as: Stream Processing Architectures, Real-time Data Architectures, Event Streaming, Data Streaming, Streaming Pipelines

Why learn Streaming Architectures?

Developers should learn streaming architectures when building applications that require real-time data processing, such as fraud detection, monitoring dashboards, or social media feeds. They are essential for handling high-velocity data from sources like sensors, logs, or user interactions, offering scalability and immediate insights compared to batch processing. Use cases include financial trading platforms, ride-sharing apps, and real-time recommendation engines.

Learning Resources

• tutorial: Confluent Streaming 101
• docs: Apache Kafka Documentation
• book: Streaming Systems Book by Tyler Akidau et al.
• video: Real-time Stream Processing with Apache Flink
• course: Coursera: Real-Time Data Streaming Specialization