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Data Mesh ... must assume, by default, the ubiquitous nature of the data. Data can be of any origin, it can come from systems within an organization, or outside, beyond the boundary of organizational trust. It can be physically served by any underlying platform on one cloud hosting or another. Data Mesh must embrace the diversity of the use cases and their unique modes of access to data. The use cases range from historical data analysis and reporting, training machine learning models and data-intensive applications. Each needs to read data in a different format, in the spectrum of graphs, files, tables and events. An ever increasing complexity of the business landscape--its diversity of functions, continuous change, and need for real-time decision making in volatile times--is the organizat...

Over the last decades, the technologies that have exceeded in their operational scale have one thing in common, they have minimized the need for coordination and synchronization.

... the foundational principles of Data Mesh -- Domain Data Ownership, Data as a Product, Self-serve Data Platform, Computational Federated Governance.

Despite the undeniable innovation, there are fundamental assumptions that have remained unchallenged for the last few decades and must be closely evaluated:

- Data must be centralized to be useful - managed by a centralized organization, with an intention to have an enterprise-wide taxonomy.

- Data management architecture, technology and organization are monolithic.

- The enabling technologies dictate the paradigm - architecture and organization.

The need for cleansing, preparing, aggregating and sharing data remains, so does the usage of data pipeline, regardless of using a centralized data architecture or Data Mesh. The difference is that in traditional data architectures, data pipelines are first-class architectural concerns that integrate to compose more complex data transformation and movement. In Data Mesh, a data pipeline is simply an internal implementation of the data domain and is handled internally within the domain.

The data product SLOs include, among others:

Interval of change

How often changes in the data are reflected.

Timeliness

The skew between the time that a business fact occurs and is served to the data users.

Completeness

Degree of availability of all the necessary information,

Statistical shape of data

Its distribution, range, volume, etc.

Lineage

The data journey from source to now.

Precision and accuracy over time

Degree of business truthfulness as time passes.

Operational qualities

Freshness, general availability, performance, etc.

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