Data Fabric Vs. Data Mesh
“Data fabric” and “data mesh” are emerging architectures for integrating, accessing and managing data across multiple heterogeneous platforms and technologies. But there are differences, so expect to hear more about both in 2022 along with some debate – and possibly some confusion.
The data fabric concept has been around for a few years, but it’s become more prevalent as data is increasingly scattered across hybrid-cloud/multi-cloud networks. Data fabrics weave together data from internal silos and external data sources to create data networks to power business applications, AI and analytics, according to a definition from Stardog, which develops an Enterprise Knowledge Graph platform.
Major big data players such as Tibco, Talend and Informatica, along with newer companies like K2, develop software used in data fabric implementations. Stardog founder and CEO Kendall Clark believes data fabric will become more mainstream in 2022, noting in an email comment that “the maturity of enterprise data fabric as the key to data integration in the hybrid multi-cloud world will become more commercially evident.”
This year “will see significant growth and interest in data fabric solutions as companies seek to leverage a common management layer to accelerate analytics migration to the cloud, ensure security and governance, and quickly deliver business value by supporting real-time, trusted data across hybrid-multi-cloud – all in driving digital transformation,” said Buno Pati, CEO at big data software vendor Infoworks, in a statement. “We believe this technology will be broadly adopted over the next five years.”
The “data mesh” concept, developed by Zhamak Dehghani, a director at IT consultancy Thoughtworks, is focused on the logical and physical interconnectedness of data from producers through to consumers, according to Starburst, which targets its data analytics engine for use within data mesh systems. Data observability software vendor Monte Carlo says data mesh is an alternative to a monolithic data lake and “embraces the ubiquity of data in the enterprise by leveraging a domain-driven, self-service design.”