Operationalizing Snowflake Intelligence Requires DataOps Automation

DataOps.live Momentum brings DataOps automation to Snowflake, ensuring trusted, AI-ready data for intelligence-driven decision-making

Why DataOps Automation Is the Missing Link

Snowflake Intelligence introduces AI-native capabilities directly into the Snowflake Data Cloud, enabling business users to interact with data through Cortex Agents that can query, reason, and act using natural language.

However, before those agents can deliver accurate insights, data teams must first create and maintain the semantic model that defines business meaning and manually configure Cortex Agents to use it. This process, built around YAML definitions and complex relationships, can be time-consuming and difficult to scale across an enterprise.

That’s where DataOps automation becomes essential. DataOps automation is the disciplined use of continuous integration, continuous delivery, observability, and governance-as-code to ensure data pipelines, models, and products move from development to production reliably and repeatably. It replaces manual, ad-hoc processes with governed, version-controlled workflows that keep data and metadata consistent across environments.

DataOps.live brings this automation to life. The DataOps.live Momentum platform applies these principles across the entire Snowflake ecosystem, automating data integration, testing, deployment, and governance so that every data product and semantic layer powering Snowflake Intelligence is trusted, production-ready, and explainable. Momentum also integrates the Metis AI Data Engineering agent, which removes the manual effort of creating semantic models by translating plain-English business definitions into governed YAML for Cortex Agents to use.

Together, Snowflake Intelligence and DataOps.live Momentum create a closed-loop solution, where automation not only generates Snowflake Intelligence capabilities across Data Products, but also empowers users to turn new AI-driven insights directly into governed Data Products in minutes. This bidirectional automation transforms how organizations move from data discovery to production-ready value. 

How DataOps Automation Powers Snowflake Intelligence

To effectively leverage Snowflake Intelligence, a data organization must establish four key layers:

1. Governed Data Foundation
2. Reliable Semantic and Agent Infrastructure
3. Strong Governance and Control Mechanisms
4. Framework For Adoption and Continuous Improvement.

The DataOps.live Momentum platform operationalizes each of these layers through automation, governance, and observability.

1. Governed Data Foundation

Snowflake Intelligence requires a strong, governed data foundation that is unified, accurate, and continuously validated.

DataOps.live Momentum automates this foundation by:

• Unifying Data Pipelines: Automating ingestion, harmonization, and transformation across structured and unstructured sources directly within Snowflake.
• Automating Governance as Code: Enforcing fine-grained access control, masking, and RBAC policies across every environment.
• Defining and Managing Business Context: Momentum captures the essential metadata and business context as part of each Data Product, ensuring that the meaning behind key measures, dimensions, and entities is consistent and traceable across the platform.
• Ensuring Continuous Observability: Built-in testing and monitoring validate data quality, schema changes, and freshness before they impact downstream AI models or agents.

2. Reliable Semantic and Agent Infrastructure

Building reliable agents requires consistent Semantic Models, reusable templates, and full version control.

• DataOps.live Momentum uses a built-in AI Data Engineering agent called Metis. Metis helps automate and simplify the creation and maintenance of semantic models, removing the need to manually craft complex YAML and accelerating the setup of Cortex Agents within Snowflake Intelligence.
• Simplified Semantic Model Creation: Within DataOps.live Momentum, the built-in Metis AI Data Engineering agent simplifies the creation of semantic models by guiding teams through defining business concepts, relationships, and measures in a structured, governed way, without requiring them to manually author complex YAML files. Momentum ensures those definitions are captured consistently and translated into the format Snowflake Intelligence needs to deliver accurate, business-aligned insights.
• Unified Agent Delivery: The DataOps.live Momentum platform packages each Semantic Model and Cortex Agent configuration as part of the data product, versioned, tested, and deployed through automated CI/CD pipelines.
• Governed Evolution: As business definitions evolve, the Metis agent within Momentum automatically identifies impacted models and pipelines, ensuring that Snowflake Intelligence agents remain aligned with current business meaning and governed data definitions.

Scale-Out Automation Across All Data Products

For organizations already running hundreds of Data Products in DataOps.live, Momentum will automatically generate Snowflake Intelligence capabilities for each one.

What would otherwise require days of manual YAML authoring and agent setup per product is now automatically created by Momentum instantly defining semantic models, relationships, and Cortex Agent configurations for every governed Data Product.

This means enterprises can enable AI-native insights at scale, turning an entire Data Product ecosystem into Snowflake Intelligence-ready assets with near-zero additional effort.

3. Strong Governance and Control Mechanisms

AI can’t scale without trust. DataOps.live Momentum ensures that governance and control are built into every Data Product and agent lifecycle:

1. Policy Enforcement: Governance policies and access rules are encoded directly into Data Product pipelines, ensuring consistent compliance and traceability.
2. Auditability and Explainability: Every deployment, model change, and agent change is logged, traceable, and explainable, enabling compliance with enterprise and regulatory standards.
3. Continuous Data and Semantic Validation: Momentum continuously tests and monitors data freshness, schema integrity, and semantic alignment. Ensuring that the inputs feeding Snowflake Intelligence remain reliable and explainable. This creates a continuous feedback loop, a true governance control plane for AI-ready data operations. Adoption and Continuous Improvement

4. Framework For Adoption and Continuous Improvement.

DataOps.live Momentum’s automation enables organizations to move from pilot to production confidently:

• Iterative Rollouts: Teams can deploy pilot agents alongside existing data products and scale progressively as patterns prove successful.
• Feedback and Retraining: Observability data feeds into improvement cycles for agents, prompts, and data pipelines.
• Cross-Functional Collaboration: With Metis translating plain-English business definitions into governed YAML, business and technical teams can collaborate directly without manual coding or YAML expertise.

The result is a foundation where AI can scale safely, governed, explainable, and continuously improving.

The Role of Metis in the Semantic Layer

DataOps.live is the leader in DataOps automation, and DataOps.live Momentum is the platform capability that operationalizes delivery, governance, and CI/CD inside Snowflake.

Built within Momentum is Metis, the AI Data Engineering agent that bridges business understanding and Snowflake Intelligence’s technical structure.

Typically, defining a Snowflake Semantic Model requires YAML expertise and deep schema knowledge. With Metis, that complexity disappears.

• You define your business logic and relationships through prompts using natural language.
• The Metis AI agent generates the governed Semantic Model YAML that Snowflake Intelligence uses.
• live Momentum validates, versions, and deploys it as part of the Data Product lifecycle.

Metis ensures the semantics powering Cortex Agents remain accurate, current, and aligned with enterprise business meaning, while DataOps automation ensures those models are deployed, governed, and monitored consistently across environments.

DataOps.live Momentum transforms the creation and management of Semantic Models from a manual, technical process into an automated, governed, and scalable discipline essential for operationalizing Snowflake Intelligence at scale.

Turning Snowflake Intelligence Insights into Data Products

This is where DataOps.live Momentum takes Snowflake Intelligence from powerful to transformational.

Imagine this: a business user explores data through Snowflake Intelligence, asking natural-language questions and uncovering a key insight, a filtered dataset, a chart, or a refined output that’s ready to share.

Now, instead of handing that insight off to a data engineering team, filing a ticket, or waiting days for a pipeline to be built, the user simply says:

“Make this into a Data Product.”

In that moment, DataOps.live Momentum, through the DataOps.live MCP Server, springs into action, automatically converting that insight into a fully governed, production-grade Data Product complete with CI/CD pipelines, infrastructure as code, automated testing, data quality rules, and data contracts.

All in minutes.
No specs. No waiting. No manual handoffs.

This closes the loop between AI discovery and governed delivery, a capability that fundamentally redefines how organizations operationalize their insights.

What once took weeks of coordination now happens instantly and securely, ensuring that every valuable AI-driven discovery can become a reusable, trusted Data Product ready for enterprise consumption.

It’s not just automation; it’s the bridge between intelligence and execution.

From AI-Ready Data to AI-Driven Organizations

Snowflake Intelligence represents the convergence of data and AI for business consumers. To succeed, it requires the same rigor, repeatability, and lifecycle management that define modern data engineering.

DataOps.live Momentum provides that foundation, automating delivery, governance, and observability of the data and semantic layers Snowflake Intelligence depends on. Once that foundation is in place, organizations can confidently activate AI through Cortex Agents and other Snowflake Intelligence features.

In short, Momentum operationalizes the data foundation, while Snowflake Intelligence operationalizes AI. 

Take the first step with Native CI/CD for Snowflake today. Learn more and get Dynamic Delivery today.