🌊Data Streaming

NEW

Continuous, low-latency processing of unbounded data flows as events arrive from sources.

DESCRIPTION

Data streaming processes unbounded data in motion with minimal latency for timely insights and operational reactions. Frameworks such as Apache Flink, Kafka Streams, and Spark Structured Streaming provide stateful operators, time semantics, and fault tolerance. Typical patterns include windowed aggregations, event-time processing, and exactly-once semantics via transactional sinks or idempotence. Streaming supports use cases like fraud detection, personalization, observability, and IoT telemetry. It contrasts with batch by emphasizing continuous ingestion and incremental computation.

ALTERNATIVE DEFINITIONS

AWS

Streaming data is the continuous flow of data generated by various sources.

Defines streaming by its continuous, source-generated nature rather than processing model.

Source

Wikipedia

Stream processing is a computer programming paradigm, equivalent to dataflow programming, that deals with the processing of data streams.

Frames streaming as a programming paradigm for dataflow over streams.

Source

RELATED TERMS

Event Streaming Apache Kafka Apache Flink Spark Structured Streaming Google Cloud Pub-Sub Windowing Exactly-Once Processing Backpressure Change Data Capture Complex Event Processing

COMPETING TERMS

Batch Processing

RELATIONS

• Broader: Streaming

KNOWLEDGE AREAS

Real-time & Streaming Data Data Engineering Data Architecture Data Services

TARGET AUDIENCE

Data Engineer Software Developer DevOps Engineer Cloud Architect Data Architect MLOps Engineer

EXTERNAL SOURCES

• Wikipedia - Stream processing
• Apache Flink - Core stream processing concepts
• Apache Kafka - Kafka Streams developer guide
• Apache Spark - Structured Streaming programming guide
• Google Cloud - Real-time analytics patterns and tools

TOP QUESTIONS

What problems does Data Streaming solve?

It enables real-time reactions and insights, such as anomaly detection, personalization, and operational monitoring, by processing events as they occur.

Category: Overview

How does Data Streaming differ from Batch Processing?

Streaming processes continuous data with low latency, while batch processes finite datasets periodically. Streaming emphasizes event-time ordering and incremental state.

Category: Comparison

What are hot topics in Data Streaming today?

Unified batch/stream APIs, exactly-once semantics, stateful scaling, change data capture, and streaming lakehouse architectures are current focal areas.

Category: Topic

What is event-time processing in Data Streaming?

Event-time uses timestamps embedded in events, combined with watermarks, to handle out-of-order data and produce correct, deterministic results.

Category: Example

What are common mistakes with Data Streaming deployments?

Ignoring backpressure, underestimating state size, and misconfiguring time semantics cause latency spikes and incorrect aggregates.

Category: Mistakes

How is exactly-once achieved in Data Streaming?

Via transactional sinks, idempotent writes, checkpointing, and deterministic processing, frameworks minimize duplicates and ensure consistent state.

Category: Requirements

What is micro-batch in Data Streaming?

Micro-batch discretizes streams into small time-based batches, balancing latency and throughput, as in Spark Structured Streaming.

Category: Overview

When should you avoid Data Streaming?

If latency requirements are relaxed, data volume is small, or complexity outweighs benefits, batch may be simpler and cheaper.

Category: Risks

Which tools are popular for Data Streaming?

Apache Kafka, Flink, Spark Structured Streaming, and Kafka Streams are widely used for stream processing and transport.

Category: Tools