The strengths and limitations of Data Mesh architectures.
The strengths and limitations of
Data Mesh architectures
Data Mesh, what is it?
- The Data Lake: as its name suggests, it refers to the provision of a volume of data that is as recent and relevant as possible. Data is read-only, loaded raw, and interpreted only when read.
- The Data warehouse: it is a storage according to pre-established schemes according to the uses that will be made of the data. The model opens as soon as it is loaded.
- Data Mesh: these are virtual gateways between databases, which are specialized by domain. The data teams will query, transform, create bridges between domains to obtain the most relevant result possible.
What are the advantages of Data Mesh architectures?
The Data Mesh will consume raw data as inputs to return them cleaned, with additional structuring. These "products" can be consumed by entities other than the data owner, and the crossovers are endless.
Decentralized data ownership.
Each domain (BU) of the company owns its data, because it knows it best and uses it the most. He must therefore take care of the collection, the cleaning, the transformations... and he has every interest in doing it well. The Data Mesh is (in theory) a guarantee of quality.
A real self-service.
The data is made accessible to all those who need to access it, there is no longer any compartmentalization or watertight silo. This is done by providing a self-service infrastructure with APIs, which is the only centralized point in the Data Mesh.