Data Management at Scale, 2nd Edition

Chapter 5. Services and API Management

Most organizations don’t want a data mesh. Instead, they want a data and integration mesh: a unified approach to holistically manage both the analytical and operational planes. Why? Because there are significant benefits to gain from aligning the two planes. The operational data architecture contrasts with the analytical data architecture because the operational plane processes commands and requires predictability and real-time processing of small datasets, while the analytical plane focuses on data reads and requires complex data analysis, which uses large datasets and isn’t that time-critical. However, there’s a large amount of overlap in the domain model, how APIs (like data) should be treated as products, and how the boundaries between applications and domains should be set. Your business capabilities are the same when looking at your architecture through an operational or analytical lens. The applications that provide these capabilities have teams behind them that manage them and ensure they stay up and running. The language that the team uses for development is the same. Ultimately, the unique context that influenced the application design matches the context that influences the design of your data products and APIs. In addition, the shift toward loosely coupled applications and autonomous, agile teams within API management is similar to what underpins the shift toward a data mesh architecture. It therefore makes sense to apply similar ...