SpotOn reduces time to actionable insights by 6x with Snowflake, dbt Cloud, and Metaplane
SpotOn's Postgres database was chronically undersized and slow, causing ETL job failures, delayed BI reporting, and making advanced analytics impossible. Only two engineers could contribute to data modeling, the team could not keep pace with data requests, and the data stack became a bottleneck as the company scaled.
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 · Transactional data ingested
The data team ingests transactional data from payment processor partners.
SpotOn achieved a 600% decrease in time to actionable insights, an 8x increase in engineering output, and $110,500 in savings. Data model contributors grew 750% from two to 17, model creation time dropped from days to hours, and time to identify data quality issues dropped from hours or days to seconds.
What failed first
SpotOn's custom Python/Airflow pipelines required advanced engineering skills and were too slow and fragile: backfilling historical data in Postgres took days or a week, models delivered with no quality guarantee had to be rebuilt from scratch if requirements changed, and Postgres could not handle large-scale aggregations or geospatial cross-joins.