StreamingPipeline

A streaming pipeline is a data processing architecture designed to continuously ingest, process, and deliver data as it arrives, typically with low latency. It handles data in motion rather than in discrete batches, enabling real-time or near-real-time insights and actions.

Core components include data sources or producers that generate events, a transport layer such as a message broker, a processing layer that applies transformations and stateful computations, and a sink layer that writes results to databases, data warehouses, dashboards, or other systems. An orchestration and monitoring layer manages the pipeline’s lifecycle, reliability, and observability, coordinating parallelism, retries, and scaling.

Processing semantics in streaming pipelines often involve event-time processing with watermarks to handle out-of-order events, windowing for aggregations over time ranges, and state management for joins, aggregations, and enrichment. Fault tolerance is a key design goal, with supports for exactly-once or at-least-once delivery, checkpointing, and replay capabilities to recover from failures or to reprocess data.

Design considerations include handling schema evolution, ensuring idempotent sinks, managing backpressure, and scaling through parallelism. Pipelines are typically implemented using stream processing frameworks (for example, Apache Flink, Apache Spark Structured Streaming, or Apache Beam) atop platforms such as Kafka, Google Cloud Dataflow, or AWS Kinesis, often with a separate storage layer for durable state and results.

Common use cases encompass real-time analytics, monitoring and alerting, fraud detection, and streaming ETL for data warehouses. Challenges involve balancing latency and throughput, handling late data, maintaining data quality, and managing operational complexity.