A data mesh is a new approach that evolves beyond traditional methods of data accessibility. It is a way of thinking about the data based on the decentralized architecture.
It improves the organizational ability to empower data producers and consumers with accessibility and management without expert team intervention. It mainly focuses on distributing the data ownership among the teams who can individually manage the data as a separate product.
It also reduces the bottlenecks without sacrificing the data governance aspects. It also addresses the few limitations of data warehousing and data lake. It also targets to elevate the business outcomes w.r.t data-centric solutions of modern data architecture that improves organizational capability on the decision making.
Data mesh is driven and governed by four major principles.
It eases the way of starting the transformational journey from a domain-driven approach to data ownership which tackles some of the hard problems around domain boundaries.
It is one of the significant pillars for growing an innovation culture where data is readily available for experimental purposes.
It also ensures compliance and security aspects while delivering data insights.
Organizations are currently in an experimental phase with different technologies as they attempt to build a data mesh for specific use cases.
However, it is rare that the entire organization has adopted the data mesh as the whole and sole solution for maintaining their data.
There is no clear way to define the implementation approach to the data mesh, but here are a few recommendations while implementing the data mesh solution:
Data Mesh is an effective way to implement an enterprise data platform. Data mesh is best for large and complex organizations with a set up of independent business units. Not all organizations can implement data mesh.
