Unraveling Data Architecture: Data Fabric vs. Data Mesh
In this first post of this year, I would like to talk about modern data architectures and specifically about two prominent models, Data Fabric and Data Mesh. Both are potential solutions for organisations working with complex data and database systems.
While both try to make our data lives better and try to bring us the abstraction we need when working with data, they are different in their approach.
But when would be best to use one over the other? Let's try to understand that.
Definitions Data Fabric and Data Mesh
Some definitions first. I come up with these after reading up on the internet and various books on the subject.
Data Fabric is a centralised data architecture focusing on creating a unified data environment. In it's environment data fabric integrates different data sources and provides seamless data access, governance and management and often it does this via tools and automation. Keywords to remember from the Data Fabric definition are centralised, unified, seamless data access.
Data Mesh, is a paradigm shift, it is a completely different way of doing things. Is based on domains, most likely inspired by the Domain Driven Design (DDD) example, is capturing data products in domains by decentralising data ownership (autonomy) and access. That is, the domains are the owners of the data and are responsible themselves for creating and maintaining their own data products. The responsibility is distributed. Key words to take away from Data Mesh are decentralised, domain ownership, autonomy and data products.
Criteria for Choosing between Data Fabric and Data Mesh
Data Fabric might be preferred when there is a need for centralised governance and control over the data entities. When a unified view is needed across all database systems and data sources in the organisation or departments. Transactional database workloads are very suitable for this type of data architecture where consistency and integrity in their data operations is paramount.
Data Mesh can be more suitable for organisational cultures or departments where scalability and agility is a priority. Because of its domain-driven design, Data Mesh might be a better fit for organisations or departments that are decentralised, innovative, and require their business units to swiftly and independently decide how to handle their data. Analytical workloads and other big data workloads may be more suitable to Data Mesh data architectures.
Ultimately, the decision-making process between these data architectures hinges on the load of data processing and the alignment of diverse data sources. There's no universal solution applicable to all scenarios or one size fits all. Organisations and departments in organisations operate within unique cultural and environmental contexts, often necessitating thorough research, proof of concept, and pattern evaluation to identify the optimal architectural fit.
Remember, in the realm of data architecture, the data workload reigns supreme - it dictates the design.