Streaming

Streaming is a data processing technique where data is continuously generated, transmitted, and processed in real-time or near-real-time as it becomes available, rather than in batches. It enables applications to handle unbounded data streams, such as sensor data, financial transactions, or social media feeds, with low latency. This approach is fundamental for building responsive systems that require immediate insights or actions based on incoming data.

Also known as: Real-time processing, Data streaming, Event streaming, Stream processing, Continuous data flow

Why learn Streaming?

Developers should learn streaming to build applications that demand real-time data processing, such as fraud detection, live analytics, IoT monitoring, or video streaming services. It's essential for scenarios where data volume is high and latency must be minimized, allowing for immediate decision-making and user interactions. Mastering streaming concepts helps in designing scalable systems that can handle continuous data flows efficiently.

Learning Resources

• docs: Apache Kafka Documentation
• docs: Stream Processing with Apache Flink
• tutorial: Real-Time Data Processing with Apache Spark Streaming
• book: Streaming Systems Book by Tyler Akidau et al.
• course: Introduction to Streaming Data on Coursera