Master's thesis: Mesh up your data architecture

A data mesh architecture is not immediately a solution that fits for every organization and situation. How can you mitigate these challenges?  

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Required interest(s)

  • Domain Driven Design
  • Microservices architecture

What do you get

  • A challenging assignment within a practical environment
  • € 1000 compensation, € 500 + lease car or € 600 + living space
  • Professional guidance
  • Courses aimed at your graduation period
  • Support from our academic Research center at your disposal
  • Two vacation days per month

What you will do

  • 65%  Research
  • 10%  Analyze, design, realize
  • 25%  Documentation

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.

  • Is there a certain technical or organizational precondition?
  • What are the challenges at an organization where data mesh might fit as a solution?
  • In which situation would a data mesh not suit the organization or the problem?
  • Which preconditions from DDD or a Microservices architecture are or are not the same for a data mesh?
  • Are organizational changes required for a data mesh?

About Info Support Research Center

We anticipate on upcoming and future challenges and ensure our engineers develop cutting-edge solutions based on the latest scientific insights. Our research community proactively tackles emerging technologies. We do this in cooperation with renowned scientists, making sure that research teams are positioned and embedded throughout our organisation and our community, so that their insights are directly applied to our business. We truly believe in sharing knowledge, so we want to do this without any restrictions.

Read more about Info Support Research here.


  1. 1
  2. Kennismakingsgesprek

    Bespreek (studie) loopbaan, interesses en ambities en introductie Info Support.

  1. 2
  2. Beoordelingen

    Assessment van professionele kennis en persoonlijkheid (capaciteit, competenties en motieven).

  1. 3
  2. Selectie interview

    Professionele kennis en persoonlijkheid verdiepen.

  1. 4
  2. De ondertekening van een contract

    Contractaanbieding en uitnodiging voor tekenmomenten.