💧Streaming Data

NEW

Continuously generated event data processed incrementally with low-latency pipelines.

DESCRIPTION

Streaming data arrives as a continuous flow from sources like applications, sensors, logs, and transactions. Systems process events in near real-time using stream processors, windowing, and stateful operations. Platforms such as Kafka, Flink, and pub/sub services enable scalable ingestion and processing. Correct handling involves backpressure, exactly-once semantics, and time synchronization. Streaming powers real-time analytics, monitoring, personalization, and anomaly detection.

ALTERNATIVE DEFINITIONS

AWS

Streaming data is data that is generated continuously by thousands of data sources, which typically send the data records simultaneously, and in small sizes (kilobytes).

Emphasizes continuous generation and small, simultaneous records from many sources.

Source

Confluent

Streaming data is the continuous flow of data generated by different sources and delivered in real time.

Focuses on real-time delivery across diverse sources.

Source

Microsoft Learn

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

Defines the processing model underpinning streaming data use cases.

Source

RELATED TERMS

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

COMPETING TERMS

Batch Processing

RELATIONS

• Broader: Data

KNOWLEDGE AREAS

Real-time & Streaming Data Data Engineering Observability & Telemetry Data Integration & Interoperability

TARGET AUDIENCE

Data Engineer MLOps Engineer AI Engineer Software Developer Data Architect

EXTERNAL SOURCES

• Wikipedia - Stream processing
• Apache Kafka - Core stream platform documentation
• Apache Flink - Flink stream processing concepts
• Google Cloud - Pub/Sub messaging overview
• Microsoft Learn - Azure Stream Analytics introduction

TOP QUESTIONS

What distinguishes Streaming Data from batch data?

Streaming processes continuous event flows with low latency; batch processes static datasets on a schedule.

Category: Definition

Which technologies are commonly used for Streaming Data?

Apache Kafka, Apache Flink, Spark Structured Streaming, and managed pub/sub services are prevalent.

Category: Definition

What are windowing and watermarking in Streaming Data?

Windowing groups events by time; watermarking tracks event-time progress to handle out-of-order data.

Category: Requirements

What is backpressure in Streaming Data pipelines?

Backpressure controls input rates when downstream stages slow, preventing overload and data loss.

Category: Definition

How do exactly-once semantics apply to Streaming Data?

They ensure each event affects state exactly once despite retries, requiring idempotence and transactional processing.

Category: BestPractice

What risks exist with Streaming Data systems?

Incorrect time handling, poor scaling, and insufficient observability cause lateness, drops, and inconsistent results.

Category: Risk

How to start implementing Streaming Data?

Define event schema, choose a stream platform, implement processing with tests, and monitor lag and throughput.

Category: HowTo

What is event-time vs processing-time in Streaming Data?

Event-time uses the time an event occurred; processing-time uses when the system processed it.

Category: Definition

How is Streaming Data observed and monitored?

Track consumer lag, throughput, error rates, and watermark progress; instrument with metrics and traces.

Category: Management