🌊Streaming

NEW

Continuous processing of data in motion as events arrive, enabling low-latency analytics and reactive applications.

DESCRIPTION

Streaming processes unbounded event data continuously, focusing on low-latency ingestion, transformation, and analysis. Systems employ time semantics, windowing, and exactly-once processing to ensure correctness. Typical platforms include Kafka, Flink, and Spark Structured Streaming. Use cases span fraud detection, personalization, monitoring, and IoT telemetry. Compared to batch, streaming emphasizes incremental computation, stateful operators, and resilience in the presence of out-of-order events.

ALTERNATIVE DEFINITIONS

Wikipedia

Stream processing is the processing of data in real time as it is produced or received.

Highlights real-time processing of data as it arrives.

Source

Confluent

Stream processing is the practice of taking action on a series of data at the time the data is created.

Emphasizes acting on data at creation time for real-time value.

Source

RELATED TERMS

Stream Processing Event-Driven Architecture Complex Event Processing Apache Kafka Apache Flink Spark Structured Streaming Event Sourcing Change Data Capture Windowing Low Latency

COMPETING TERMS

Batch Processing Micro-batching

RELATIONS

• Narrower: Data Streaming Event Streaming Spark Streaming Structured Streaming

KNOWLEDGE AREAS

Real-time & Streaming Data Data Engineering AI Data Science Observability & Telemetry

TARGET AUDIENCE

Data Engineer MLOps Engineer AI Engineer Software Developer Solution Architect Data Architect

EXTERNAL SOURCES

• Wikipedia - Stream processing
• Apache Flink - Flink streaming concepts overview
• Apache Spark - Structured Streaming programming guide

TOP QUESTIONS

Why is Streaming important?

It enables real-time insights and actions, reducing latency between data creation and decision-making.

Category: Relevancy

How does Streaming differ from Batch Processing?

Streaming processes events continuously; batch processes bounded datasets at intervals. Streaming prioritizes low latency and incremental state.

Category: Misunderstanding

What are common mistakes in Streaming systems?

Ignoring event-time semantics, failing to handle late data, and under-sizing state storage lead to correctness and scalability issues.

Category: Mistakes

What are the key requirements for Streaming reliability?

Exactly-once semantics, idempotent sinks, checkpointing, and backpressure handling are critical for robust pipelines.

Category: Requirements

What risks arise in Streaming architectures?

Operating stateful, always-on systems increases operational complexity, cost, and incident blast radius.

Category: Risks

What tools are commonly used for Streaming?

Apache Kafka for messaging, Flink and Spark for processing, and ksqlDB for streaming SQL are common choices.

Category: Example

What is windowing in Streaming?

Windowing groups events by time or count to aggregate unbounded streams into manageable computations.

Category: Example

What is a current hot topic in Streaming?

Unified batch/stream APIs and stateful serverless streaming are key developments.

Category: Topic

How do I start with Streaming?

Begin with a simple event ingestion pipeline, define SLAs, choose event-time semantics, and implement basic windowed aggregations.

Category: Management