Which service would you use for real-time data ingestion in a machine learning scenario?

Azure Event Hubs is designed specifically for high-throughput, real-time event ingestion, making it highly suitable for scenarios where data must be collected continuously as it is generated. It efficiently handles large volumes of data streams from various sources in real-time, allowing data scientists to stream data into machine learning models or analytics pipelines without delays. This capability is crucial for machine learning applications that rely on up-to-date information to drive insights, make predictions, or provide feedback loops for improving model accuracy.

In contrast, Azure Data Factory is primarily an ETL (extract, transform, load) service used for data integration and batch data processing, which is not optimized for real-time data ingestion. Azure Queue Storage is designed for message storage and retrieval in a queue format, aiding in asynchronous communication between applications but not directly tailored for the type of high-volume data ingestion required in real-time scenarios. Azure Cosmos DB, while it does support real-time data, is fundamentally a database service rather than a streaming service; its primary function is data storage and retrieval, not ingestion. Therefore, for real-time data ingestion specifically in machine learning, Azure Event Hubs stands out as the ideal choice.