DataStream

DataStream is a term used to describe a sequence of data elements that are made available over time. In computing, a data stream is typically unbounded, continuous, and produced by one or more data sources such as sensors, logs, transactions, or user activity. Unlike static, stored datasets, data streams are often analyzed on the fly, enabling near real-time insights.

Streaming data processing (DSP) refers to techniques and systems for continuously ingesting, processing, and analyzing data as it arrives. Core operations include filtering, transforming, aggregating, and joining streams, often with windowing to compute over recent data. Processing can be stateless or stateful, the latter requiring maintenance of intermediate results across events. Time semantics matter: processing time versus event time, with mechanisms like watermarks to handle late data and out-of-order arrivals.

Architectures typically separate data producers, a durable transport or broker, and a stream processing engine. Durable storage and checkpointing enable fault tolerance and exactly-once processing in many systems. Common patterns include micro-batching and true streaming, depending on the framework.

Common platforms and tools: message brokers such as Apache Kafka and AWS Kinesis provide durable, append-only logs for streams. Stream processing engines such as Apache Flink, Apache Spark Structured Streaming, Apache Storm, and Google Cloud Dataflow execute continuous computations. Many systems integrate with data warehouses and visualization tools for real-time dashboards.

Applications include real-time analytics, monitoring and alerting, fraud detection, recommendation systems, and IoT telemetry.

Challenges include handling latency, ensuring ordering and completeness, late-arriving data, backpressure, scalability, and operational complexity.