Lies, damn lies and statistics

Entries for category "1. IT Architecture"

Data Autonomy - Holistic Data Mesh

Standard event driven integration practice is to take data from a system, transform into a middle model and then transform to each destination. Data Autonomy simply says that while we have the data in the middle form, lets save it.

Each business domain must be free to evolve and mature independently as the view of significant business objects evolves. The data governance group with data stewards and SMEs should be responsible for producing domain aligned data product definitions which are then realized into data products. The features of these data products should cover interfaces and persistence for both operational and analytical data.

Enterprise Microservices

If you have a "microservice" which exposes an API calling a back end system, you don't have a microservice - you have an API. Your API has wisely used some of the concepts of microservice architecture. Microservice architecture is not an integration architecture, it's an application architecture and applications are responsible for business logic. This distinction is important because the beauty of microservice architecture is both technical and behavioural.

However, I'll extend an olive branch to the API people, no doubt aghast by my blasphemy, by calling them "integration microservices".

Integration microservices are fine I guess, but too often they are used, just as integration is often used, to sweep the legacy monster under the carpet so we don't have to see it - out of sight, out of mind. We implement integration microservices containerised, auto-scaling, auto-healing etc, IT management has an orgasm, we all add something to our CVs and the legacy monster waits.

Data Autonomy Overview

Data Autonomy is a holistic data strategy which is based on the rationale discussed in Vestigial Technology. This is an overview of the concepts.

The purpose of Data Autonomy is to counteract increasing costs and loss of flexibility in IT as the business grows and changes. Data Autonomy limits IT complexity by providing loosely coupled, consistent, accessible, and secure information anytime, anywhere, on any device.

Data Autonomy works best in conjunction with modern IT practices like automated regression, DevOps and infrastructure as code. We must embrace change and be good at implementing small changes regularly.