The concept of a data mesh provides new ways to address common problems around managing data at scale. Zhamak Dehghani has provided additional clarity around the four principles of a data mesh ...

Any organization that is serious about digital transformation should at least consider applying the data mesh’s four fundamental pillars.

One of the strongest examples of an early pioneer in data mesh, a highly decentralized data architecture, is not a telecommunications firm, large-scale manufacturing business or even a financial ...

For example, data mesh observes that today’s data pipeline teams lack context of the domain and that is problematic. So one would think domain awareness would be an appealing attribute.

Piethein Strengholt, author of Data Management at Scale, recently published an article presenting six data-mesh governance topologies and domain granularity. Each topology adapts the data mesh ...

Jeff Fried, director of product management at InterSystems, led the Data Summit session, 'Viewing Data Fabric and Data Mesh as Complementary,' to explore the ways in which data fabric and data mesh ...

When data gets pumped into one warehouse, the system can be clogged, and the data becomes less decipherable and useful for the business. By using the right data framework and harnessing the power of ...

Data mesh inverts this model with domain-driven design and product thinking. Responsibilities are distributed to the people who are closest to the data.

Databricks launches Lakehouse Federation to enhance cross-platform data query and governance in enterprise data infrastructure.

Using data meshes and fabrics When weighing the pros and cons, it’s important to keep in mind that data mesh and data fabric are concepts — not technologies — and aren’t mutually exclusive.