The tissue connecting these domains and their associated data assets is a universal interoperability layer that applies the same syntax and data standards. Importantly and somewhat controversially, this means according to traditional data mesh principles, the domain teams own the underlying platform or data storage layer. 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.
None of my business meme science software#
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. 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.Īs first defined by Zhamak Dehghani in 2019, 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. 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.įortunately, 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. 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. 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. Your boss was one of the first in the industry to see the potential in Snowflake and Looker.
None of my business meme science driver#
Your company, for one, views data as a driver of innovation.
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. But what is a data mesh and why should you build one? Inquiring minds want to know. Ask anyone in the data industry what’s hot these days and chances are “data mesh” will rise to the top of the list.
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