What is a Data Mesh and Why Should You Build One?
Ask anyone in the data industry what’s hot these days and chances are “data mesh” will rise to the top of the list. But what is a data mesh and why should you build one? Inquiring minds want to know.
In the age of self-service business intelligence, nearly every company considers themselves a data-first company, but not every company is treating their data architecture with the level of democratization and scalability it deserves.
Your company, for one, views data as a driver of innovation. Your boss was one of the first in the industry to see the potential in Snowflake and Looker. Or maybe your CDO spearheaded a cross-functional initiative to educate teams on data management best practices and your CTO invested in a data engineering group. Most of all, however, your entire data team wishes there were an easier way to manage the growing needs of your organization, from fielding the never-ending stream of ad hoc queries to wrangling disparate data sources through a central ETL pipeline.
Underpinning this desire for democratization and scalability is the realization that your current data architecture (in many cases, a siloed data warehouse or a data lake with some limited real-time streaming capabilities) may not be meeting your needs.
Fortunately, teams seeking a new lease on data need look no further than a data mesh, an architecture paradigm that’s taking the industry by storm.
Much in the same way that software engineering teams transitioned from monolithic applications to microservice architectures, the data mesh is, in many ways, the data platform version of microservices.
As first defined by Zhamak Dehghani, a ThoughtWorks consultant and the original architect of the term, a data mesh is a type of data platform architecture that embraces the ubiquity of data in the enterprise by leveraging a domain-oriented, self-serve design. Borrowing Eric Evans’ theory of domain-driven design, a flexible, scalable software development paradigm that matches the structure and language of your code with its corresponding business domain.
Unlike traditional monolithic data infrastructures that handle the consumption, storage, transformation, and output of data in one central data lake, a data mesh supports distributed, domain-specific data consumers and views “data-as-a-product,” with each domain handling their own data pipelines. The tissue connecting these domains and their associated data assets is a universal interoperability layer that applies the same syntax and data standards.
Instead of reinventing Zhamak’s very thoughtfully built wheel, we’ll boil down the definition of a data mesh to a few key concepts and highlight how it differs from traditional data architectures.
At a high level, here is a data mesh example:
A data mesh architecture diagram is composed of three separate components: data sources, data infrastructure, and domain-oriented data pipelines managed by functional owners. Underlying the data mesh architecture is a layer of universal interoperability, reflecting domain-agnostic standards, as well as observability and governance. (Image courtesy of Monte Carlo.
