Since these teams have no real interactions or collaborations, the process lacks cross-functional understanding, and many potential insights that could be extracted from the data are lost. These highly specialized data and ML (machine learning) platform engineers would then process the data and package it for teams of multi-disciplinary data consumers. In the past, data producers, usually operating in multi-disciplinary teams, were disconnected from the data-and from platform engineers who process their data. This approach starkly contrasts the traditional siloed data approach. In essence, the data in your data mesh is domain-oriented and serves as a useful product available to other domains. These other teams’ access the data in a self-service manner, extracting what they need, when they need it, in real-time. This novel approach allows organizations to efficiently manage data in a manner that is easily scalable.Īn effective data mesh allows all teams within an organization to take complete ownership of their data and package it in a useful way for other teams to access. With a data mesh, software engineering best practices and techniques are paired with the lessons learned in developing robust and resilient internet-scale applications. Simply put, it is a self-service design that is domain-oriented. Some see it as an integral part of the data democratization process because it allows various teams to access relevant, valuable data in real-time. Data mesh is the latest architectural shift in the data world.
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