Metadata Discovery Problem - Millions of connected devices like connected cameras and streaming videos are introduced to smart cities every year, which are valuable source of information. However, such rich source of information is mostly left untapped. Thus, in this paper, we propose distributed deep neural networks (DNNs) over edge visual Internet of Things (VIoT) devices for parallel, real-time video scene parsing and indexing in conjunction with BigQuery retrieval on stored data in the cloud. The IoT video streams parsed into adaptive meta-data of person, attributes, actions, object, and relations using pre-trained DNNs. The meta-data cached at the edge-cloud for real-time analytics and also continuously transferred to the cloud for data fusion and BigQuery batch processing. The proposed distributed deep learning search platform bridges the gap between edge-to-cloud continuum computation by utilizing state-of-the-art distributed deep learning and BigQuery search algorithms for the geo-distributed Visual Internet of Things (VIoT). We show that our proposed system supports real-time event-driven computing at 122 milliseconds on virtual IoT devices in parallel, and as low as 2.4 seconds batch query response time on multi-table JOIN and GROUP-BY aggregation.