{"slug": "snowflake-moves-enterprise-ai-beyond-fragmented-data-pipelines", "title": "Snowflake moves enterprise AI beyond fragmented data pipelines", "summary": "Snowflake Inc. is building a governed data layer on Amazon Web Services Inc. to help enterprises move AI into production without replicating data, according to Zahir Gadiwan, partner solution engineering leader at Snowflake. Gadiwan said the real challenge for enterprise AI is getting trusted data, business context, governance, and scalable infrastructure to work together, and that Snowflake's Cortex Analyst and Cortex Agents apply semantic context to Iceberg tables on AWS.", "body_md": "Data interoperability is quickly becoming a practical requirement for companies trying to move artificial intelligence into production.\n\nPicking the right model or adding computing capacity is only part of the job. Companies also need reliable data that carries the right business meaning and remains protected as it moves between systems. Snowflake Inc. is building its [Amazon Web Services integration](https://www.snowflake.com/en/why-snowflake/partners/all-partners/aws/) around that need. The goal is to give businesses a governed data layer that supports AI and other enterprise workloads without forcing them to create more copies of their information. That approach is changing how companies think about the data beneath their AI systems, according to [Zahir Gadiwan](https://www.linkedin.com/in/zgadiwan/) (pictured), partner solution engineering leader at Snowflake.\n\n“When customers talk about enterprise AI, the real challenge usually is not finding another model,” Gadiwan said. “The challenge is getting trusted data, business context, governance and scalable infrastructure to work together in one operating model.”\n\nGadiwan spoke with theCUBE’s [John Furrier](https://www.linkedin.com/in/furrier/) during an interview for the [AWS Marketplace Series](https://siliconangle.com/aws-marketplace/) on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how Snowflake and Amazon Web Services Inc. are helping companies [connect governed data](https://aws.amazon.com/marketplace/pp/prodview-3gdrsg3vnyjmo?trk=38dfef57-f0a9-4f6e-aa91-62f83e1ef891&sc_channel=el) while limiting unnecessary movement between platforms. *(* Disclosure below.)*\n\n### Data interoperability changes how AI accesses enterprise information\n\nTraditional data architectures often depend on copying information into separate applications and processing engines. That model becomes expensive and difficult to manage when AI systems need accurate information from several environments at once. A governed data layer gives services access to information where it already lives while preserving security controls and limiting duplication, Gadiwan explained.\n\n“The architectural shift is from moving data into every engine, app and AI workflow, to creating a governed data layer on AWS that multiple services can access in place,” he said. “Snowflake and AWS make that possible through open interoperability patterns across storage, catalog, streaming and AI services.”\n\nThe cost of the old approach can add up quickly. Every new copy requires storage and often another pipeline to maintain. It can also slow analysis and leave teams wondering which version of the data is correct, Gadiwan emphasized. By separating storage and compute from the systems used to govern the data, businesses can choose new services without rebuilding their entire data environment each time their needs change.\n\n“For years, the default answer was move the data. Let’s copy the data. Let’s rebuild the pipeline. And then let’s govern it later,” he added. “That creates a lot of latency. It creates cost. It creates data duplication. It creates data trust issues. What customers want now is a governed data architecture on AWS where the same trusted data can support analytical use cases, applications and AI workloads without being constantly replicated.”\n\n### Semantic context helps AI understand governed data\n\nConnecting databases is not enough. [AI systems also need context](https://siliconangle.com/2026/06/11/snowflake-enterprise-ai-production-snowflakesummit/) that explains what the information represents and how it relates to the business. They must also respect the permissions already attached to that data. Snowflake Cortex Analyst and Cortex Agents are designed to apply this context to Iceberg tables on AWS as well as information stored inside Snowflake, Gadiwan noted.\n\n“Once the data is connected, Cortex Analyst combines the Iceberg tables with Snowflake tables to create a semantic view,” he said. “This is where we add business meaning so AI can reason over sales and marketing data in business terms instead of raw table structures.”\n\nSnowflake can [make that governed context available](https://signin.aws.amazon.com/signin?redirect_uri=https%3A%2F%2Faws.amazon.com%2Fmarketplace%2Fprocurement%3FproductId%3D106ff316-230f-4ad7-9d64-91e400266426%26redirectUrl%3Dhttps%25253A%25252F%25252Faws.amazon.com%25252Fmarketplace%25252Fpp%25252Fprodview-3gdrsg3vnyjmo%26sc_channel%3Del%26trk%3D38dfef57-f0a9-4f6e-aa91-62f83e1ef891%26tryForFree%3Dtrue%26isauthcode%3Dtrue&client_id=arn%3Aaws%3Aiam%3A%3A015428540659%3Auser%2Fawsmp-contessa&forceMobileApp=0&oauth_region=us-east-1) to outside tools through integrations with Amazon Q and the Model Context Protocol. Employees can ask questions in natural language across different data sources, but they still see only the information their existing permissions allow. The interface becomes easier to use without weakening control over company data, Gadiwan pointed out.\n\n“It is data in open standards, in Iceberg, data in Snowflake, data outside of Snowflake,” he said. “But in a very governed, auditable fashion, you are giving access to users, what they can see, what they cannot see based on their privileges and expanding that across the AWS ecosystem with Snowflake.”\n\nHere’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the [AWS Marketplace Series](https://siliconangle.com/aws-marketplace/):\n\n*(*Disclosure: TheCUBE is a paid media partner for the AWS Marketplace Series. Neither AWS, the sponsor of theCUBE’s coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)*\n\n##### Photo: SiliconANGLE\n\n# A message from John Furrier, co-founder of SiliconANGLE:\n\nSupport our mission to keep content open and free by engaging with theCUBE community. **Join theCUBE’s Alumni Trust Network**, where technology leaders connect, share intelligence and create opportunities.\n\n**15M+ viewers of theCUBE videos**, powering conversations across AI, cloud, cybersecurity and more** 11.4k+ theCUBE alumni**— Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network.\n\n# Are you AWS customer? 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Our new proprietary theCUBE AI Video Cloud is breaking ground in audience interaction, leveraging theCUBEai.com neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations.", "url": "https://wpnews.pro/news/snowflake-moves-enterprise-ai-beyond-fragmented-data-pipelines", "canonical_source": "https://siliconangle.com/2026/08/11/data-interoperability-ai-pipelines-awsmarketplaceseries/", "published_at": "2026-08-11 20:20:51+00:00", "updated_at": "2026-08-11 20:35:13.825772+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-infrastructure"], "entities": ["Snowflake Inc.", "Amazon Web Services Inc.", "Zahir Gadiwan", "John Furrier", "theCUBE", "SiliconANGLE Media", "Cortex Analyst", "Cortex Agents"], "alternates": {"html": "https://wpnews.pro/news/snowflake-moves-enterprise-ai-beyond-fragmented-data-pipelines", "markdown": "https://wpnews.pro/news/snowflake-moves-enterprise-ai-beyond-fragmented-data-pipelines.md", "text": "https://wpnews.pro/news/snowflake-moves-enterprise-ai-beyond-fragmented-data-pipelines.txt", "jsonld": "https://wpnews.pro/news/snowflake-moves-enterprise-ai-beyond-fragmented-data-pipelines.jsonld"}}