streamingstorage

Streaming storage is a data storage model designed to persist and expose continuous sequences of data as they are generated, such as events, logs, or telemetry. It emphasizes high-throughput ingestion, low-latency access, and durable, ordered delivery. In practice, streaming storage is implemented as an append-only, log-like structure where records are stored with monotonically increasing offsets or timestamps, enabling replay and time-based querying.

Architectures typically organize data into partitions or segments, each acting as an immutable sequence of records. Data is written once and read by consumers that subscribe to a particular stream or partition. Retention policies determine how long records are kept, while compaction, indexing, and tiered storage optimize space and query performance. Replication across nodes or zones provides fault tolerance, and offset management supports consumer progress tracking and fault recovery.

Common data models in streaming storage include event logs and time-series data, where each record carries metadata such as a timestamp, key, and payload. Delivery guarantees vary by system, with at-least-once, at-most-once, and, in some cases, exactly-once semantics depending on configuration and tooling. Schema evolution and compatibility, as well as tooling for windowing and aggregations, are important considerations for real-time analytics and streaming pipelines.

Use cases include real-time analytics, log aggregation, telemetry collection, monitoring, and streaming ETL. Prominent systems that embody streaming storage patterns include distributed log services and message brokers, such as Apache Kafka, Apache Pulsar, and managed services like Amazon Kinesis, all of which provide durable, replayable streams that support scalable consumption.