Data mesh vs data fabric: understanding the popular data architectures
The metamorphosis of data in driving more intuitive solutions has made business leaders begin to understand the importance of continuously building or exploring data management strategies. Though Gartner listed data fabric in its top trends for data analytics in the 2021 report, data experts believe that even the data mesh architecture has tapped into the potential of leveraging data to fetch more valuable and actionable business insights. These architectures enable enterprises to handle and share different data from heterogeneous data sources.
In the data-connected world, the two are used interchangeably, thus, it is crucial to understand the fundamental difference between the two strategies to incorporate the right one.
Expersight defines a data fabric as “a design concept which serves as an integrated layer of data and connecting processes. A data fabric utilises continuous analytics over existing, discoverable and referenced metadata to support the design, deployment and utilization of integrated and reusable datasets across all environments, including hybrid and multi-cloud platforms.”
Technology centric: The data fabric architecture integrates data in the existing infrastructure and adds a layer of additional operating technology layer that integrates all the data together and prepares the data to get meaningful insights proactively.
Data is a by-product: The data fabric architecture identifies and integrates real-time data from different sources to a specific destination or location through data-integration technologies. Thus, this architecture treats data as a by-product of the process of moving and integrating data at a centralised location.
Centralised: Data fabric provides a single unified platform to coordinate data management across multiple sources and technologies. This architecture helps to enhance data governance, establish standardised security policies for all connected APIs and ensures homogenised protection across different data end-points. It offers centralised data security and governance policies which are implemented consistently across varied environments.
Undistributed ownership: Data fabric provides a single unified platform for handling data across multiple technologies. The centralised form of integrating different data and systems sometimes formats data differently and thus downstream data consumers such as data scientists, data engineers, and data analysts. However, what is to be noted is that data management is unified and not the actual storage.
Data mesh architecture is a new strategy of decentralising the data and bringing ownership to each business domain such as sales or customer support. The objective of data mesh is to establish coherence between data coming from different domains across an enterprise.
Organisation Centric: Data mesh focuses on bringing organisational change in the data architecture and extrapolates the current infrastructure with new deployments in business domains. Thus, brings a smarter and more competent way of utilising human efforts in data management.
