Database calls are fast and the curation of objects lends itself to query operators but does not take advantage of the progressive and rolling sequence of objects detected frame-by-frame or its bookkeeping along with multiple UAV sequence tracking which is something messaging paradigm and event processors have solved successfully in several data and telemetry pipelines and from a traditional grounding that databases are queues. With the shift in paradigm from rows to events and similar SQL operators across both such as with Flink, the DFCS drone video sensing platform does not demand adherence to a database or messaging paradigm if the interface supports the following requirements: 1. standard query operator on objects detected with world co-ordinates attributes. 2. participation in retrieval augmentation along with a vector store and search and 3. support analytics stacks with programmability that can support custom drone sensing applications built independent of the UAV swarm sensing-analysis-routing architecture dedicated to the swarms’ flights. In addition, some criteria are suggested for messaging pipelines which include:

1. Noise filtering: This involves sifting through data to spotlight the essentials.

2. Long-Term data retention: this involves safeguarding valuable data for future use

3. Event-trimming: This customizes data for optimal analytics so that the raw data is not dictating eccentricities in the charts and graphs.

4. Data condensation: this translates voluminous MELT data into focused metrics and prevents archiving or cleaning up as messages are removed from the queue.

5. Operational Efficiency Boosting: This amplifies operating speed and reliability.

This article implements a sample for this alternative with emphasis on multiple stream processors