The client was undergoing a significant architectural shift, moving from a traditional data warehouse model to a Data Mesh paradigm. To support this transition, they required a robust Data Governance framework to ensure data assets were discoverable, high-quality, and properly managed across decentralized teams. Specifically, the team lacked a unified way to publish data assets for organization-wide consumption and a method to validate ”data in motion” within their engineering pipelines.

• • • Unable to manage the high volume of interdependencies and resolve daily technical overlaps.
    • Shifting to Data mesh was slower and the transition was hard to adapt to new ways of handling the data.
    • Difficulty in user engagement tracking and hit analytics in the data catalog that were immature, limiting deep insights and advanced reporting.

• • To facilitate data discovery, the team utilized Microsoft Purview on the Azure cloud.
  • This served as the central hub where data assets were published along with sample data, metadata, and business glossaries.
  • Our team built a data observability and quality module. Rather than starting from scratch, we added a Java-based package to an existing in-house tool called ”Simple”. 
  • Data engineering pipelines consumed this new framework to validate data as it moves.
  • Users were able to define data quality rules and manage alerts through a dedicated web application based on their credentials.
  • PowerBI was implemented as the reporting module to track rule definitions, alerts, and overall system health.