MediaRadar | Vivvix achieves 150% increase in hourly ad throughput with Databricks Mosaic AI and Spark Structured Streaming
MediaRadar | Vivvix's ad classification of over 6 million unique products relied on manual operators and was bottlenecked by Amazon SQS polling constraints, leaving the company unable to keep pace with rapidly growing ad volumes or meet SLAs, while fragmented infrastructure across multiple pods made model monitoring nearly impossible.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Real-time ad ingestion
Spark Structured Streaming enables continuous, automated real-time data ingestion from video ads without manual intervention.
Tools used
DatabricksDatabricks Mosaic AISpark Structured StreamingRay clusterWhisperOCRDatabricks Model ServingUnity CatalogAmazon Simple Queue Service (SQS)
Outcome
After adopting Databricks, MediaRadar | Vivvix increased ad classification throughput from 800 to 2,000 creatives per hour — a 150% improvement — and reduced model experimentation from two days to half a day, while eliminating SLA concerns.
What failed first
MediaRadar | Vivvix's existing ML models and in-house fine-tuned model were insufficient for the scale and diversity of over 6 million unique products due to lack of training data, and their SQS-based polling setup could not meet SLAs.