“The data fabric does a beautiful job of encompassing three concepts needed to create applications and processes: the data catalog, the data model, and data access,” says Sanat Joshi, executive vice president of product and innovations at Appian. “But now add business rules, process models, APIs, security groups, the organizational model, and their interrelationships into one unified view of the enterprise, and that becomes your context layer.”
Integrations with data fabrics
Devops teams just getting started on an AI agent proof of concept may want to connect directly to the optimal data sources and APIs. Michel Tricot, CEO and cofounder at Airbyte, says connecting agents to live APIs is a great start, but it creates two big problems: APIs only return data that an agent already knows to ask for, and every query is an expensive API call chain that, with overhead, can overwhelm infrastructure in production volumes.
Tricot says the data fabric for AI use cases must be dynamic, leveraging discovery of available information from replicated data, fetching live contextual information, and writing the data back to business applications to update records.
