Guide to Snowflake Cortex Analyst and Semantic Models

Self-service analytics has long been the goal for organizations seeking to democratize data and reduce dependency on data teams. Traditional business intelligence tools often fall short—business users grapple with complex SQL queries, dashboards require manual updates, and even so-called "self-service" tools still demand technical expertise. 

Snowflake Cortex Analyst addresses this challenge by allowing users to query data using natural language and receive instant, accurate results. We are excited about the potential of Snowflake Cortex Analyst and want to share our insights on the opportunities it presents for true self-service analytics and what organizations need to start leveraging Snowflake Cortex Analyst.

Table of Contents
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• What is Snowflake Cortex Analyst?
  • Cortex Analyst vs Cortex Agent
  • How Cortex Analyst Works
• Why Consider Using Cortex Analyst?
• The Role of Semantic Models in Cortex Analyst
  • What is a Semantic Model?
  • How to Build a Semantic Model for Snowflake Cortex Analyst?
• Building Semantic Models for Snowflake Cortex with Select Star
• Reimagine Self-Service Data Exploration with Cortex Analyst

What is Snowflake Cortex Analyst?

Snowflake Cortex is a suite of AI-powered services designed to bring generative AI and machine learning directly into the Snowflake ecosystem. It includes components like Cortex Analyst, Cortex Agent, and Cortex Search, each catering to different AI-driven use cases.

Cortex Analyst is part of Snowflake Cortex. It functions as an AI-powered BI assistant, translating natural language into optimized SQL queries. This capability relies on pre-trained large language models to interpret user intent, semantic models to ensure accurate query generation, and direct integration with Snowflake for secure, governed data access.

Cortex Analyst vs Cortex Agent

Differentiating from its counterpart, Cortex Agent, Analyst focuses specifically on converting natural language queries to SQL for structured data analysis. It's optimized for single-turn Q&A on structured data, ensuring high accuracy by leveraging predefined semantic models. This makes it ideal for business intelligence and self-service analytics use cases.

How Cortex Analyst Works

Cortex Analyst acts as an AI-powered BI assistant that translates natural language into optimized SQL queries. It relies on:

• Pre-trained LLMs to interpret user intent
• Semantic models to ensure accurate query generation
• Direct integration with Snowflake for secure, governed data access

By mapping business terms (e.g., "customer revenue last quarter") to actual Snowflake tables and columns, it enables non-technical users to retrieve insights without needing SQL expertise.

Why Consider Using Cortex Analyst?

Cortex Analyst addresses a critical challenge in self-service analytics: guiding business users to retrieve insights without SQL expertise. By enabling teams to ask ad-hoc questions and get immediate answers, it reduces reliance on data teams and accelerates decision-making. 

Real-world implementations have shown promising results. Bayer integrated Cortex Analyst into their BI environment, allowing executives to query Snowflake directly via a chat-based interface, moving beyond static dashboards to real-time answers. Similarly, Siemens Energy and Nissan have deployed Cortex Agents to enhance employee access to enterprise data. These AI assistants can answer complex multi-step queries, combining structured and unstructured data from various sources including Snowflake tables, contract documents, and knowledge bases.

Deployment options for Cortex Analyst offer flexibility to suit various organizational needs and workflows. Companies can integrate this powerful tool into their existing systems in several ways, ensuring that data insights are accessible wherever they're needed most, including:

• Embedded in BI tools – Add a natural language search bar to dashboards
• Integrated with collaboration tools – Enable analytics within Slack or Microsoft Teams
• API-powered analytics assistants – Build custom AI-driven data apps

The Role of Semantic Models in Cortex Analyst

What is a Semantic Model?

Central to Cortex Analyst's accuracy is the semantic model, a YAML-based definition that maps business concepts to the underlying database schema. This model acts as a bridge between how business users discuss data and how it's stored, enabling the LLM to generate correct SQL queries.

How to Build a Semantic Model for Snowflake Cortex Analyst?

Creating an effective semantic model involves defining logical tables, relationships, metrics, and synonyms that reflect the business terminology in YAML files.

• Tables & columns (e.g., customers, orders)
• Relationships & joins (e.g., customers.customer_id = orders.customer_id)
• Metrics & calculations (e.g., SUM(order_amount) AS total_revenue)

Best practices include focusing on key tables and columns relevant to specific domains, providing business-friendly names and synonyms, and defining metrics at the appropriate granularity.

Snowflake offers a Streamlit-based generator that extracts metadata from Snowflake and generates a starter YAML file for Cortex Analyst. Example snippet:

tables:
  - name: orders
    columns:
      - name: order_id
        type: integer
      - name: order_amount
        type: float
    metrics:
      - name: total_revenue
        expr: SUM(order_amount)
relationships:
  - name: orders_to_customers
    left_table: orders
    right_table: customers
    join_key: customer_id

Building Semantic Models for Snowflake Cortex with Select Star

While Snowflake offers tools to help generate semantic models, the process can still be time-consuming. Data teams would need to reverse engineer relationships, metrics, and business terms for each table and column, a process that can take weeks or even months for a large, complex data environment. For organizations that have not already defined semantic models for their data, this becomes an even more daunting task. 

This is where data catalog and metadata management tools can play a crucial role. Platforms with automated data catalogs like Select Star can significantly accelerate the process of building semantic models for Cortex Analyst. By ingesting and analyzing metadata, usage logs, and query history, these tools can reverse engineer and automatically generate relationships, metric definitions, and business terminology mappings.

For example, Select Star automatically discovers that orders.customer_id links to customers.customer_id by analyzing foreign key relationships or SQL join patterns, and uses these relationships as the primary and foreign keys in the semantic model, without requiring any manual effort. Select Star also tracks and adds table and column descriptions, and automatically generates them if the source is empty. If business glossary terms or column descriptions are already stored in the catalog, we directly incorporate them into the semantic model’s metrics and sample values—ensuring the model reflects the organization’s established business terminology.

Reimagine Self-Service Data Exploration with Cortex Analyst

Snowflake Cortex Analyst is a compelling option for organizations looking to scale self-service analytics without compromising security or governance. Unlike traditional BI tools, Cortex Analyst runs directly on your data within Snowflake, ensuring that all existing access controls, data masking, and row-level policies are automatically respected—no data movement required. It offers a highly flexible interface that can be embedded into other applications or integrated into workflows, making it easier to bring natural language querying capabilities to where your users already work. 

With support for multi-turn conversations and semantic model awareness, business users can explore data intuitively while staying aligned with curated definitions. Because it’s built natively on the Snowflake platform, it also benefits from enterprise-grade scalability, low-latency responses, and unified observability alongside your other Snowflake workloads. Altogether, Cortex Analyst reduces the need for custom dashboards or complex training sessions, empowering more teams to get trusted answers faster.

The key to success with tools like Cortex Analyst lies in comprehensive semantic models that accurately reflect the business context. By leveraging metadata management and data cataloging tools in this process, organizations can accelerate their journey towards truly conversational analytics, bringing the power of data closer to subject-matter experts across the business. If you’re looking to accelerate self-service analytics with Snowflake Cortex Analyst, our team can walk you through how it works.

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