Real-time Analytics
replace slow, expensive media measurement data infrastructure so engineers and modelers could unify large household datasets and iterate on ML faster.
Reported impact
VideoAmp cut backfills from five days to 13 hours, improved performance 10x, reduced costs by 90%, and gave engineers a more unified environment for ML and analytics.
VideoAmp uses Snowflake on Snowflake to replace slow, expensive media measurement data infrastructure so engineers and modelers could unify large household datasets and iterate on ml faster. Large audience and media datasets from tens of millions of households and devices are consolidated on Snowflake for measurement and modeling. VideoAmp uses the shared platform to backfill, transform, and analyze those datasets much faster than on its prior stack, which had become expensive and slow. Engineers and data teams work from the same environment to improve ML workflows, media measurement products, and downstream analytics used across the business. Keeping ingestion, storage, transformation, and downstream access on one governed platform reduces data copying, simplifies operations, and gives teams a single trusted operating view for analytics and AI. VideoAmp cut backfills from five days to 13 hours, improved performance 10x, reduced costs by 90%, and gave engineers a more unified...
household, device, and TV measurement data flow into Snowflake; curated data and model outputs are then served to engineers, data scientists, and media measurement products.
10x Performance improvement
13 hours For backfilling data, down from 5 days
increased performance and reduced costs by 90%
uses huge volumes of data from 39 million households and 63 million devices
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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