Data Mesh - Architecture, Principles & Implementation: Build scalable, domain-led data practices through product-focused ownership

Vivek P.

₱5102.99

Video Aug 2026 1hr 23mins 1st Edition

Vivek P.

₱5102.99

Video Aug 2026 1hr 23mins 1st Edition

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Key benefits

Core Data Mesh principles, architecture, and data product concepts in one coherent visual story
Clear context for why centralized data models become difficult to scale across organizations
Vendor-neutral path from domain ownership and data products to architecture and implementation

Description

Modern organizations often outgrow centralized data models. As demand expands, bottlenecks, duplicated effort, disconnected teams, and limited scalability make trusted data harder to deliver. A domain-oriented model offers a different way to align ownership, architecture, and business context. Beginning with databases and data warehouses, the learning path follows the evolution to data lakes and lakehouses, showing why technology alone cannot resolve organizational challenges. It then introduces domain-oriented ownership, data as a product, and the four principles of Data Mesh. The progression covers data product characteristics, components, lifecycle, benefits, limitations, architecture, implementation stages, common mistakes, and connections to modern data technologies. Practical guidance helps you identify where current approaches fall short, discuss Data Mesh with technical and business stakeholders, and plan adoption without relying on a specific vendor, cloud platform, or tool. You will gain the vocabulary and architectural perspective to assess products, ownership, and platform enablement. By the end of this course, you will be able to explain how an enterprise can organize, manage, and share data through a scalable Data Mesh approach.

Who is this book for?

Data professionals, data architects, solution architects, engineers, analytics practitioners, technology leaders, and business stakeholders who need a clear view of Data Mesh and its organizational implications. Basic familiarity with data and analytics concepts is helpful, but no programming, cloud-platform expertise, or specific tools are required.