Stream ingestion and stream processing are distinct stages in handling real-time data, each serving a specific purpose. Stream ingestion focuses on collecting and transporting data from sources to a destination system, ensuring it’s reliably stored or made available for downstream use. Stream processing involves analyzing, transforming, or acting on that data in real time as it flows through the system. While ingestion is about moving data, processing is about deriving value from it.
Stream ingestion tools like Apache Kafka, AWS Kinesis, or Apache Pulsar act as pipelines. Their primary job is to handle high-throughput data from sources such as sensors, application logs, or user interactions and deliver it to storage systems (e.g., databases, data lakes) or processing engines. For example, a ride-sharing app might use Kafka to ingest GPS updates from thousands of drivers into a centralized system. Key challenges here include ensuring low latency, fault tolerance, and scalability. Ingestion systems often include features like partitioning (to parallelize data flow) and replication (to prevent data loss), but they don’t perform complex computations on the data itself.
Stream processing frameworks like Apache Flink, Spark Streaming, or Kafka Streams take the ingested data and apply logic to it. This could involve filtering noise, aggregating metrics (e.g., calculating average response times), or triggering alerts (e.g., fraud detection). For instance, a retail platform might use Flink to analyze user clickstreams in real time, identifying spikes in product views and updating recommendations instantly. Processing systems handle stateful operations (e.g., counting events over a 5-minute window) and manage complexities like out-of-order data or late arrivals. The output might feed dashboards, databases, or other services for immediate action.
While ingestion and processing are separate, they often work together. Without reliable ingestion, processing systems can’t access timely data. Conversely, ingestion alone provides raw data but no insights. A typical architecture uses Kafka for ingestion, which then feeds into Flink for processing. Developers might configure Kafka to buffer data during processing outages, while Flink ensures computations are accurate and efficient. Understanding both stages helps design systems that are both robust (ingestion) and intelligent (processing).