Data mesh vs. Data Lake: Which is the optimal choice for generative AI applications?
Since 2019, the data mesh paradigm has been widely adopted in the field of data architecture and data management. This new concept introduced by zhamak dehghani advocates for the shift in data ownership — from a central data teams to a domain-business team who has the deepest understanding of the data.
The centralisation of enterprise data in one place which some years ago was straightforward and seems logical has become complex and difficult to implement and manage over the time. This complexity primarily arises from the exponential growth of data and varying data requirements across different departments.
Managing enterprises data solely through a central team has become a bottleneck in many companies; Besides maintaining data infrastructure, and ensuring smooth data flow between departments, the central team had a pivotal role in the creation of analytics based products tailored to serve dedicated departments. Over the time, prioritisation was no longer sufficient to deal with the different expectations coming from different businesses. This led to a general frustration and, more critically, a loss of competitive advantage. Empowering business-level data ownership within each department has proven to be a catalyst for increased speed, flexibility, and efficiency in…
