What type of data integration approach involves real-time data processing?

The streaming data integration approach is characterized by its capability for real-time data processing. In this method, data is continuously ingested, processed, and dispatched in real-time as soon as it becomes available. This is particularly beneficial for applications that require immediate insights or actions based on incoming data, such as monitoring systems, fraud detection, and real-time analytics.

Streaming integration allows organizations to react to market changes, user interactions, or operational issues as they happen, thereby enabling a proactive rather than reactive business strategy. This type of integration typically leverages technologies like Apache Kafka, Amazon Kinesis, or similar platforms designed to handle high-throughput, low-latency data flows.

In contrast, batch processing focuses on processing large volumes of data at specific intervals rather than continuously, making it less suitable for scenarios that demand immediacy. ETL and ELT methods, while they can be part of a data integration strategy, typically involve loading data into a staging area first, which can add latency to the process compared to streaming.