Stream ingestion and stream processing are two distinct concepts within the realm of data streaming. Stream ingestion refers to the collection and initial entry of real-time data into a system. This involves capturing data from various sources, such as IoT devices, social media feeds, transaction logs, or user interactions, and ensuring that it is transmitted to a data storage solution or processing engine. The focus during this phase is on efficiently receiving and transferring data, typically using frameworks like Apache Kafka, Amazon Kinesis, or RabbitMQ. For example, when data from a network of sensors is sent to a central server for monitoring and analysis, that action is classified as stream ingestion.
In contrast, stream processing involves the analysis and manipulation of data as it flows in real-time. This is where the actual data transformations, calculations, and filtering take place. Stream processing engines, such as Apache Flink, Apache Spark Streaming, or Apache Beam, take the ingested data and perform operations like aggregations, windowing, and joins to extract meaningful insights or trigger actions based on the data. For instance, in a customer analytics application, stream processing would calculate key metrics, such as the number of purchases made in the last hour or the average spend per transaction, based on incoming transaction data.
In summary, while stream ingestion is about getting data into a system, stream processing is about analyzing and understanding that data once it’s there. Both steps are essential in the lifecycle of streaming data, but they serve different purposes. Developers need to implement ingestion techniques that can handle high throughput and low latency, while also designing processing workflows that can make sense of that data in a timely manner. Together, these components enable building responsive applications that can react to events as they happen.