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Data Mesh Principles and Logical Architecture

 Data Mesh Principles and Logical Architecture The great divide of data What do we really mean by data? The answer depends on whom you ask. Today’s landscape is divided into  operational data  and  analytical data . Operational data sits in databases behind business capabilities served with microservices, has a transactional nature, keeps the current state and serves the needs of the applications running the business. Analytical data is a temporal and aggregated view of the facts of the business over time, often modeled to provide retrospective or future-perspective insights; it trains the ML models or feeds the analytical reports. The current state of technology, architecture and organization design is reflective of the divergence of these two data planes - two levels of existence, integrated yet separate. This divergence has led to a fragile architecture. Continuously failing ETL (Extract, Transform, Load) jobs and ever growing complexity of labyrinth of data pipel...
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Service Custodian

Service Custodian
Let's imagine a pretty world of SOA-happiness where the computing needs of an enterprise are split into many small applications that provide services to each other to allow effective collaboration. One fine morning a consumer service needs some information from a supplier service. The twist is that although the supplier service has the necessary data and processing logic to get this information, it doesn't yet expose that information through a service interface. The supplier has a potential service, but it isn't actually there yet.

In an ideal world the developers of the consumer service just asks the supplier service to develop the potential service and all is dandy. But life is not ideal - the sticking point here is that the developers of the supplier service have other things to do, usually things that are more important to their customer and management than helping out the consumer service team.

Recently I was chatting with my colleague Erik Dörnenburg and he told me about an approach he saw a client use to deal with this problem. They took a leaf out of the open source play-book and made all their services into internal open source systems. This allows consumer service developers write the service themselves.

I'm sure many readers are rolling their eyes at the visions of chaos this would cause, but just as open source projects don't allow just anyone to edit anything; this client uses open-source-style control mechanisms. In particular each service has a couple of custodians - people whose responsibility it is to keep the service in a healthy state. In the normal course of events the consumer developer wouldn't actually commit changes to the supplier source tree directly, instead they send a patch to the custodian. Just like an open-source maintainer, the custodian receives the patch and reviews it to see if it's good enough to commit. If not there's a dialog with the consumer developer.

As Erik knows well from his own open source work, reviewing a patch is much less effort than making a change yourself. So although the custodian approach doesn't entirely eliminate the problem of consumer developers needing to wait on supplier developers, it does a lot to reduce the difficulty. And again following the open-source model, a consumer developer can be made a committer once the custodians are comfortable. This still means that commits can get reviewed by the custodians, but avoids the custodians becoming a bottleneck.

Related to this was their approach to a service registry. We've seen a lot of fancy products being sold to provide service registry capabilities so that people can lookup services and see how to use them. This client discarded them and used a HumaneRegistry instead.

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