Which data ingestion method is appropriate for streaming data sources?

The choice of the streaming data ingestion method is appropriate for handling continuous flows of data that are generated in real-time. This method is essential for applications requiring immediate processing and analysis of data as it arrives, rather than waiting to process it in batches.

Streaming ingestion allows systems to capture data in small increments, making it possible to address scenarios such as real-time analytics, event processing, and monitoring applications. This is particularly important in use cases such as IoT sensor data, financial transactions, or user activity logs, where timely insights can significantly impact decision-making or operational efficiency.

In contrast, the other methods mentioned, including both structured and unstructured batch, are designed for processing data that is collected and stored over a period before analysis, which is not suitable for scenarios requiring immediate or near real-time data handling. Static ingestion implies dealing with fixed datasets that do not change over time, further highlighting the incompatibility with streaming data contexts. Therefore, streaming is the definitive choice for real-time data ingestion requirements.