In this article, I aim to delve into the different types of data platform architectures, taking an in-depth look at their evolution, strengths, weaknesses, and practical applications. A key focus will be on Data Mesh architecture, its role in the modern data stack (MDS), and the current data-driven landscape.
It is a well-known fact that the architecture of a data platform profoundly affects its performance and scalability. The challenge often lies in selecting an architecture that best suits your specific business needs.
Given the overwhelming multitude of data tools available on the market today, it's easy to get lost. The internet articles I see from time to time on this topic are often very speculative. Questions about which tools are the best, who leads the industry, and how to make the right choice can be very frustrating. This story is aimed at data professionals who want to learn more about designing data platforms and which one to choose in each situation.
Modern Data Stack
I keep hearing this term on almost every data-related website on the Internet. Every LinkedIn data group offers a dozen posts on this topic. However, most of them only cover data tools and do not emphasize the importance of data analytics.
