Configurable Data Platform for US Theme Park Analytics

When two theme-park operators merged, 34 parks still each ran their own SQL Server with ~2,720 source tables living on separate servers and over 10 million rows of data to migrate. Schema drift was causing failures in hard-coded ETL jobs, and there was no auditing or run level transparency, so data issues often surfaced only after flawed reports.

Inferenz built a config-driven data platform: centralizing mappings, load flags, S3 paths in a Snowflake table; using a dynamic COPY procedure with Python and Airflow; implementing object-level refresh tracking and row-count verification with alerts. Onboarding new park feeds became under two days, around 90% faster, schema drift issues dropped to zero manual fixes, and each run now logs full status and counts so leadership trusts the numbers.

Read the full case study on the Inferenz website to explore the architecture, challenges and lessons learned.

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