Data Meshes are an organizational approach to make structures such as Data Lakes or Lakehouses efficiently usable in the company. Here a three benefits for the business.

Recap: Data Lakehouse

In order to have a technically mature platform on which to build the Data Mesh, it is recommended to build a Data Lakehouse. Storing raw data in Data Lakes, while loading parts of it into the Data Warehouse for purposes like Self-Service BI or ML Services makes it a Data Lakehouse. The Data Lakehouse should combine the advantages of Data Lakes and Data Warehouses into a hybrid concept. The two systems are not operated side by side, but as a novel single system. Benefits of a Data Lakehouse could be [1]:

• Decoupling data storage from data processing to achieve better scalability.
• Open standardized storage formats and interfaces.
• Support for different data types, from unstructured to structured data.
• Support for various workloads, such as data science, machine learning, SQL, and analytics.
• End-to-end streaming: streaming support eliminates the need for separate systems to serve real-time data applications.
• Shorter time-to-value compared to a Data Warehouse.

The Data Mesh Approach

A Data Mesh approach could improve the Data Lakehouse as the dominant architectural paradigm. It is important to understand that the Data Mesh concept primarily establishes a new organizational perspective and is less based on technical problem solving. Therefore, you should consider this four principles when building up a Data Mesh organization [3]:

• Domain-oriented decentralized data ownership and architecture: A Data Mesh should serve the individuals business units. Herefore, one or different Data Lakehouses could be build.
• Data as a product: The Data Lakehouse architecture helps to manage data as a product by providing different data team members in domain-specific teams complete control over the data lifecycle.
• Self-serve data infrastructure as a platform: Users can supply themselves with data in a self-service BI tool, while data scientists, for example, access the same data and develop models.
• Federated computational governance: The data should be backed up and distributed with a role concept. Data catalogs are also helpful here, for example.

To understand what is technically and organizationally really necessary, I recently wrote an article on how to build a data mesh in the Azure Cloud — read more here.