Stream Processing
Processing data continuously as it arrives, rather than in batches. Powers real-time analytics, fraud detection, and live dashboards.
What is Stream Processing?
Processing data continuously as it arrives, rather than in batches. Powers real-time analytics, fraud detection, and live dashboards.
Stream Processing is a advanced concept that sits in the Stream & Batch Processing area of system design. Engineers reach for it whenever they need to reason about real-world trade-offs in that space — not just for textbook correctness, but because real production systems at companies like Netflix, Amazon, and Google make these decisions every day.
If you want to go deeper than this definition — with diagrams, code, and a quiz to lock it in — work through the "Stream Processing" lesson linked below. It walks through the why, the mechanism, the trade-offs, and how the giants actually use it in production.
Learn Stream Processing in depth
Full interactive lesson with diagrams, code examples, real-world references, and a quiz.
Open the Stream Processing lessonRelated lessons
Lessons that touch on Stream Processing as part of a larger topic.
Design a Real-Time Analytics Dashboard
Design a real-time analytics system - stream processing, WebSocket-powered dashboards, aggregation pipelines, and windowed computations at scale
capstone · capstone
Stateless Stream Processing
Transform events independently, filtering, mapping, and routing without maintaining state
advanced · stream batch processing
Stateful Stream Processing
Maintaining state across events, aggregations, joins, and pattern detection in streaming data
advanced · stream batch processing
Exactly-Once Semantics
Processing every event exactly once despite failures, the holy grail of stream processing
advanced · stream batch processing
Kappa Architecture
Everything through a single stream processing layer, simplifying Lambda by eliminating the batch layer
advanced · stream batch processing
See also
Related glossary terms you might want to look up next.
Batch Processing
Processing large volumes of data in scheduled chunks rather than in real time. Think nightly reports, ETL jobs, and data warehouse loads.
Kafka
A distributed event streaming platform that handles millions of events per second. Used by LinkedIn, Netflix, and Uber for real-time data pipelines.
Event Sourcing
Storing every state change as an immutable event instead of just the current state. You can rebuild any past state by replaying events.