Google Cloud Professional Data Engineer Exam 2025 – 400 Free Practice Questions to Pass the Exam

A stream processing architecture is primarily defined by its ability to handle continuous data streams and process them in real-time. This approach allows organizations to ingest, process, and analyze data as it is generated—enabling timely insights and immediate reactions to changing data. Applications of stream processing include real-time analytics, monitoring, and immediate data-driven decisions, which are critical in various industries such as finance, telecommunications, and IoT.

The capability of stream processing systems to manage inflows of data from multiple sources and perform computations on-the-fly distinguishes them from batch processing, which focuses on processing large amounts of historical data at periodic intervals. This real-time processing ensures that users can respond to events as they happen, making it essential for applications requiring timely data responsiveness. Furthermore, the architecture inherently supports scalability and flexibility, accommodating changing data volumes and processing needs efficiently.