We are Eddytor, a small and dedicated team with a vision to build the world's most simple master data tool. We felt there was a need for a tool that users could get started with quickly, including only the most essential master data features, making them easy and straightforward to execute.

In a data-driven world, analytics platforms rely heavily on data from multiple IT systems. Yet, there’s always a small yet critical piece of information that is either missing or doesn't reside in any established IT system—be it master data, specific mappings, or customized tables. This data, often crucial for producing complete analytics insights, typically resides in the minds or files of business users. They are the ones who know what’s needed, while data engineers are the ones who need it integrated into a data platform to deliver meaningful, automated analytics solutions back to the business.

Without a dedicated solution, the responsibility for this data management often falls on the data and analytics team. To fill in these gaps, teams turn to quick-fix tools like Excel, Power Apps, or SharePoint lists, which are better suited for individual tasks but tend to fall short for master data needs. The result is usually a workflow that is messy and time-consuming, requiring a substantial investment of effort with little return in terms of efficiency or clarity. Over time, this approach leads to fragmented, error-prone data management that costs more than it contributes.

Eddytor is designed to bridge this gap, seamlessly connecting business users and data engineers to support their shared goals on platforms like Databricks and Microsoft Fabric. Eddytor leverages Delta table technology — an industry-standard championed by both Databricks and Microsoft Fabric — as its default structure, making data management more streamlined, accessible, and reliable. By tightly integrating business input directly into the analytics workflow, Eddytor ensures that master data updates can be made effortlessly and directly by the people who understand it best. This structure removes the bottlenecks of traditional tools, helping data engineers spend less time on manual data wrangling and more on developing robust, automated analytics solutions for the business