The problem
Each location operated its own AlpineIQ instance. Reporting involved exporting data, importing it into Google Sheets, and assembling the analysis. Comparing performance across locations meant repeating that collection work.
What we built
We built an integration that loaded data from the separate accounts into Snowflake, then recreated the reports the team already relied on.
- Connected the location-specific AlpineIQ accounts.
- Loaded source data into a shared warehouse.
- Recreated the existing reports and enabled analysis across locations.
What changed
Reports on demand. A shared view across locations.
The integration removed the manual collection step and gave the team more flexibility in how it queried its data. Recreating familiar reports also made the transition easier for the people using them.
The technical decisions
Load first, transform in the warehouse.
The pipeline followed an ELT approach: data was loaded before transformations were applied. That kept the source information available for different reporting needs.
Keep the useful parts of the old workflow.
The team already knew what it wanted from its Google Sheets reports. Rebuilding those views provided a familiar starting point while removing the repeated exports.