In this article, I aim to delve into the various types of data platform architectures, taking a better look at their evolution, strengths, weaknesses, and practical applications. A key focus will be the Data Mesh architecture, its role in Modern Data Stack (MDS) and today’s data-driven landscape.
It’s 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 aligns with your specific business needs.
Given the overwhelming multitude of data tools available in the market today, it’s easy to get lost. The Internet articles I see now and then on this topic are often highly speculative. Questions about which tools are best, who leads the industry, and how to make the right choice can be very frustrating. This story is for data practitioners who would like to learn more about data platform design and which one to choose in each scenario.
Modern data stack
I keep hearing this term on almost every data-related website on the Internet. Every single LinkedIn data group offers a dozen of posts on this topic. However, the majority of those cover just the data tools and don’t emphasize the importance of…