Real-time ingestion is the continuous insertion of new vectors into a live database as data arrives, as opposed to loading data in scheduled bulk batches. It describes the flow of data into the system, ensuring that fresh content is captured and made available with minimal delay.
It is the natural fit for streaming data sources — incoming messages, sensor readings, user activity, freshly published content — where waiting to accumulate a batch would introduce unacceptable lag. Real-time ingestion pipelines embed and store each item as it comes in, often paired with real-time indexing so the new vectors are not just stored but immediately searchable.
The engineering considerations are throughput, ordering, and consistency: the system must keep up with the incoming rate, handle bursts, and ensure new data is reliably committed and indexed. Real-time ingestion is essential for applications that must reflect the latest state of the world, and it complements batch ingestion, which remains the more efficient choice for loading large, static datasets all at once.