We built Eddytor because master data and mappings too often live in people’s heads or in ad‑hoc files. Analytics runs on data from many systems, but that small, critical piece—reference data, lookups, custom tables—never made it into a proper platform. The result is bottlenecks, manual wrangling, and data that doesn’t scale.

Business users know what’s needed. Data engineers need it inside Databricks or Fabric. We wanted a simple tool that connects both worlds: web-based, with only the essentials, so teams can start quickly and keep data consistent and reliable.

At Eddytor, we make that bridge a reality.

Business input feeds straight into the analytics workflow, so the people who understand the data can update it where they already work. That removes the usual bottlenecks, so data engineers spend less time on manual wrangling and more on building robust, automated analytics for the business.