How Data Mesh Architecture changed our Engineering Teams

As a seasoned data engineer, I’ve witnessed firsthand the limitations of traditional centralized data platforms, especially in healthcare. This led my team to reimagine our data architecture using the data mesh paradigm.

In a centralized architecture, data is extracted from sources then conformed and integrated in a single data warehouse. The goal is to create one “source of truth” that serves all analytics use cases.

Centralized Data Warehouse

This works well initially, but becomes unwieldy at scale. We struggled with schema conflicts, technical debt, tight coupling, and slow iteration as more use cases and data sources were added.

Enter data mesh, pioneered by companies like Uber and Alibaba. In a data mesh:

• Data is managed and served by domain-oriented teams called “data products”
• Each data product offers self-describing APIs, events, and access points for its domain
• Data discovery and governance is federated across data products
• Data is queried via a virtual layer rather than materialized integration

Data Mesh Architecture