For enterprise development teams, the same reduced data movement could mean spending less time building pipelines to move supported Oracle data into Snowflake and more time building applications that use it, said Stephanie Walter, practice leader of AI stack at HyperFrame Research.
Data professionals, meanwhile, could spend less time maintaining the extract-and-load jobs needed to move data between the two platforms, freeing them to focus on higher-value analytics and AI projects, Chaturvedi said.
Similarly, for CIOs, the same reduction in data movement and integration complexity could help address data readiness and cost challenges that often slow enterprise AI initiatives, Chaturvedi added: “Making ERP data available to AI tools faster and simplifying procurement through a single contract and bill could reduce some of the barriers to adoption.”
