Data mesh and data lake are both modern approaches to data architecture, but they differ in several key ways.
A data lake is a centralized repository that is used to store large volumes of structured, semi-structured, and unstructured data. The goal of a data lake is to provide a centralized source of truth for data within an organization, enabling teams to analyze and gain insights from large volumes of data. However, data lakes can be challenging to manage, as they require significant resources to ensure data quality, governance, and security.
In contrast, a data mesh is a decentralized approach to data architecture that emphasizes the ownership and management of data by individual teams or domains within an organization. In a data mesh, data is treated as a product, with each team responsible for building and managing its own data products. This approach promotes greater agility, flexibility, and scalability in managing and analyzing data, while also promoting a culture of data ownership and accountability.