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Config-Driven Data Platform for a US Based Theme Park

created by theme park merger

visiting the theme parks annually

with 10M+ rows migrated

When two leading park operators merged, each of the 34 venues still ran its own SQL Server. Data teams faced these problems

Source Fragmentation 2,700 tables lived on separate park servers, so teams pulled numbers from different snapshots
Schema Drift New or dropped columns crashed hard-coded jobs, triggering late-night fixes
Zero Audit Trail No run-level log showed row counts or errors, so failures stayed hidden until reports looked wrong

We built a config-driven pipeline that gathers data from all 34 park servers into one Snowflake warehouse.

Central Config Table All mappings, S3 paths, and load flags now sit in a Snowflake table that analysts can edit
Object-Level Refresh Tracking Start time, end time, status, and duration are captured for every table, every run
Row-Count Verification Source and target totals are compared on the fly. Any mismatch triggers an alert
Dynamic COPY Procedure A stored procedure builds COPY INTO commands for each table and writes outcomes to an audit log
Airflow-Orchestrated Loads Python tasks move files from SQL Server to S3, call the dynamic COPY, then archive each batch

Impact Delivered

New park feeds move from request to production in under two days

Schema drift handled automatically; engineers focus on analysis

Every run logs row counts and status, so executives trust the numbers