Stream Processing

Definitions

1. Processing data continuously as it arrives rather than in periodic batches. Handles unbounded datasets using windowing (tumbling, sliding, session) for aggregation. Frameworks include Apache Flink, Kafka Streams, and Apache Beam. Enables real-time dashboards, fraud detection, and event-driven architectures.

   In plain English: Processing data immediately as it flows in, rather than waiting to collect a batch and process it all at once.

   Example: "We moved fraud detection from batch (hourly) to stream processing — now we flag suspicious transactions in under 2 seconds."

Related Terms

• Game Loop
• State
• Event Sourcing
• Data Lakehouse
• Operational Transform
• Data Access Layer
• Data Access Object
• Data Binding
• Data Flow
• Redis Pub/Sub