A data mesh architecture is not immediately a solution that fits for every organization and situation. How can you mitigate these challenges?
What do you get
What you will do
Data becomes more and more important to businesses. Having the right data in the right place at the right time with the right quality to support business decisions, optimization, automation and powering AI models. And on the other hand, the customers are also more engaged and curious about more information about their favorite products; where does this product come from? What are the ingredients? And so forth.
For many situations and organizations, a possible first step is a modern data platform with centralized storage. With the ever-growing amount of data, centralization is not always an option as it is more difficult to scale in terms of team, lead time, server and compute at scale.
We see a movement emerging where the data is being decentralized towards specific business domains, where a different data architecture is needed. A data mesh architecture seems to be a promising architecture because of the scalability of the data platforms and the decentralization. A development that can be compared in software architecture with a Microservices architecture and Domain Driven Design (DDD). But what is involved in making that transition, for which organization or situation is this interesting or not and how do you start this transition?
The data mesh architecture is most comparable to a Microservices architecture and Domain Driven Design in Software Engineering. Now a Microservices architecture is not immediately a solution that fits for every organization and situation.
There are specific situations where this does and does not match as a solution, as there are also new challenges when you implement a Microservices architecture.
But what about the data mesh? How can you mitigate these challenges of the data mesh?
What are the preconditions for an organization where the data mesh architecture is a good solution.
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