Snowflake Data UI Guide - Choose the Right Tool for Every Analytics Use Case

Introduction

It's been about two years since I joined Snowflake, and the transformation I've witnessed in data analytics UI has been nothing short of remarkable. Back in early 2024, when it came to working with data in Snowflake, your options were essentially Snowsight worksheets and dashboards, plus third-party BI tools. Streamlit in Snowflake (hereafter referred to as SiS) and Snowpark Container Services (hereafter referred to as SPCS) had just launched but weren't yet mainstream.

Fast forward to today, and the landscape looks completely different. Snowflake Intelligence, Snowflake Workspaces, Snowflake Notebooks, Snowflake Managed MCP Server, Cortex Code, Vercel v0 integration - the list of new options keeps growing. In my daily conversations with customers, one question has become increasingly common: "With all these tools available, which one should we use?"

That's exactly what this article aims to answer. I'll provide a comprehensive overview of every UI option for working with data in Snowflake, covering their strengths, limitations, target users, and ideal use cases. Whether you're a business user, data analyst, data scientist, or developer, I hope this guide helps you find the right tool for your needs!

Note (2026/2/5): Some features mentioned in this article are still in development or Public Preview. They may be significantly updated in the future.

Note: This article represents my personal views and not those of Snowflake. The tool evaluations and use case classifications are based on my personal experience and may vary depending on your organization and project requirements. Please treat this as one perspective.

The Evolution of Snowflake's Data UI

As I mentioned, in early 2024, worksheets, dashboards, and BI tools were the primary ways to interact with Snowflake data. In just two years, this has diversified dramatically. Workspaces has evolved worksheets into a full IDE experience. Intelligence enables business users to query data using natural language without writing SQL. Notebooks brings data science workflows entirely within Snowflake. And with SiS Container Runtime, you can now run lightweight dashboards at a fraction of the cost. On the AI front, Copilot has evolved into Cortex Code, and MCP Server enables AI Agent integration with Snowflake data.

Behind this evolution are two major trends: democratization of data access and AI-native analytics experiences. Data utilization, once dominated by data engineers and analysts, is now extending to business users - with AI serving as the bridge.

UI Options at a Glance

Here's a categorized overview of every UI option available for working with data in Snowflake.

Let's dive into each category in detail.

Snowflake Native UI (Analytics)

Tools available within Snowsight for analyzing and exploring data using SQL or natural language.

Snowflake Workspaces

Overview:
Snowflake Workspaces is an integrated development environment (IDE) available within Snowsight. GA since September 2025, it represents a significant evolution from the traditional worksheet experience. Originally focused on SQL editing, Workspaces now supports Notebooks running directly within the environment, unifying SQL and Python/Notebook development. Shared Workspaces became GA in January 2026 for team collaboration, and Notebooks in Workspaces went GA in February 2026.

Pros:

• Zero-setup: start working immediately
• Unified environment for SQL, Python (Notebooks), and dbt
• Cortex Code AI assistance for natural language code generation and editing
• Git integration for version control
• Shared Workspaces for team collaboration
• Role-based access control (RBAC) for easy permission management
• Parallel query execution from a single file
• Share pre-built queries with your team so anyone can run them anytime and view the latest data in tabular format

Cons / Considerations:

• Not suited for advanced visualizations

Best Use Cases:

• Ad-hoc SQL / Python analysis
• Data pipeline development and debugging
• dbt project development and execution
• ML model prototyping (via Notebooks)
• Schema design and data modeling
• Team collaboration
• Sharing standardized queries for day-to-day data monitoring

Snowflake Intelligence

Overview:
Snowflake Intelligence is an AI-native analytics platform that lets you ask questions about your data in natural language. Business users can perform data analysis directly without specialized SQL knowledge. Under the hood, Cortex Agents serve as the core AI orchestrator, combining the following tools to fulfill requests:

• Cortex Analyst: Text2SQL analysis on structured data
• Cortex Search: Hybrid search (vector search + keyword search + semantic reranking) on unstructured data (documents, etc.)
• Custom Tools: User-defined Stored Procedures and UDFs (User Defined Functions)

Cortex Agents interpret user requests, select the appropriate tools, execute them, and generate responses - delivering an agentic analytics experience.

Pros:

• No SQL required - analyze data with natural language
• Fully contained within Snowflake for security
• Easy to scale across the entire organization
• Semantic Models unify business terminology and data lineage
• Cross-analyze structured and unstructured data

Cons / Considerations:

• Requires upfront preparation of Semantic Models / Semantic Views

Best Use Cases:

• KPI reporting for executives
• Self-service analytics for sales/marketing teams
• Instant answers to recurring business questions
• Analysis combining internal documents and data

Snowflake Notebooks

Overview:
Snowflake Notebooks is a Jupyter-style notebook environment available within Snowsight. It supports interactive analysis combining Python, SQL, and Markdown, and you can create Streamlit-powered visualizations directly within cells. GA since November 2024.

Two runtime options are available:

Pros:

• Complete data science environment within Snowflake
• Python libraries (pandas, matplotlib, scikit-learn, etc.) available
• Create interactive Streamlit visualizations directly in cells
• Cortex Code AI assistance for natural language code generation and editing
• Easy documentation of analysis processes
• Built for team collaboration
• Snowpark ML integration for ML workflows
• Container Runtime enables deep learning and large-scale ML with GPU (PyTorch, TensorFlow pre-installed)

Cons / Considerations:

• Some feature differences compared to local Jupyter Notebooks
• Container Runtime requires External Access Integration setup for external packages

Best Use Cases:

• Exploratory Data Analysis (EDA)
• ML model prototyping
• Deep learning model training and inference with GPU (Container Runtime)
• Creating and sharing analysis reports

Snowflake Native UI (Apps)

Tools for building and running business-user-facing applications, accessible from Snowsight. Typically, developers build the apps and business users consume them.

Streamlit in Snowflake (SiS)

Overview:
SiS is a service for developing and deploying web applications using Python. You can rapidly build data apps without any frontend knowledge. Apps are created, edited, and run directly from Snowsight, with full integration into Snowflake's authentication and authorization.

Pros:

• End-to-end app development using only Python
• Full integration with Snowflake authentication and authorization
• Rapidly build custom analytics tools for business users
• Easy to integrate AI capabilities (chat, summarization, classification, etc.) via Cortex AI
• Cortex Code AI-assisted coding in the Snowsight editor

Cons / Considerations:

• Limited flexibility for highly custom UI designs
• For cost optimization or flexible package management, consider Container Runtime below

Best Use Cases:

• Custom analytics dashboards and visualization apps for teams
• Data entry and update applications
• AI/ML model demo apps
• Internal self-service analytics tools

SiS Container Runtime (Preview)

Traditional SiS uses Warehouse Runtime, but a Container Runtime option is now also available (currently in Preview). Container Runtime executes app code on SPCS while queries run on a separate warehouse.

The development experience is identical to standard SiS. You write and deploy code in the same Snowsight SiS editor, and Cortex Code AI-assisted coding is fully available. You can create, edit, and run apps without worrying about the underlying runtime differences.

Key Benefits of Container Runtime:

This is especially impactful for always-on dashboards and lightweight visualization apps from a cost perspective. If you need a team dashboard that runs continuously within Snowflake, SiS Container Runtime is a compelling choice - low cost and built entirely in Python.

For a deep dive into SiS Container Runtime, check out my previous article: SiS Container Runtime - Run Streamlit Apps at a Fraction of the Cost

Snowflake Native Apps

Overview:
Snowflake Native Apps is a framework for developing and distributing packaged applications on Snowflake. Multiple distribution methods are available: public distribution via Snowflake Marketplace, direct distribution to specific accounts via Private Listing, and distribution to non-Snowflake customers via Reader Accounts. Apps can use SiS for the UI or build custom frontends with SPCS.

Pros:

• Distribute apps and data as a package
• Consumers use apps + data without copying data
• Continuous update delivery
• Monetization via Marketplace

Cons / Considerations:

• Relatively steep learning curve
• Primarily for ISVs and data providers

Best Use Cases:

• Commercial application distribution
• Data Product offerings
• Cross-organization solution sharing

Snowpark Container Services (SPCS)

Overview:
SPCS is a fully managed service for running containerized applications on Snowflake. The core value proposition is "bringing compute to the data, rather than moving data to the compute." Deploy Docker images and run ML inference or custom applications with GPU/CPU resources - all within Snowflake. Available on AWS, Azure, and GCP.

Pros:

• Data never leaves Snowflake: Meets security and compliance requirements
• Any programming language or library (Docker-compatible)
• GPU support for ML model inference and private LLM execution
• No Kubernetes knowledge required for container operations
• Inherits Snowflake's security, governance, and access controls
• Can serve as the backend for Native Apps

Cons / Considerations:

• Requires containerization knowledge
• More setup effort compared to SiS
• External internet access disabled by default (requires External Access Integration)

When to choose SiS vs. SPCS:

• Simple dashboards and visualization apps → SiS
• Custom apps, ML inference, batch processing, non-Python languages → SPCS

Best Use Cases:

• ML model inference endpoints (including private LLM execution)
• Batch data processing with sensitive data
• Applications requiring custom runtimes
• Migrating existing Docker apps to Snowflake
• Native Apps backend

AI-Assisted Development

Tools that leverage AI to accelerate data and app development.

Cortex Code

Overview:
Cortex Code, released in February 2026, is a Snowflake-native AI coding agent - a truly revolutionary next-generation coding agent that transforms the Snowflake development experience. It is positioned as the successor to Snowflake Copilot, significantly expanding and evolving Copilot's capabilities. If you've been using Copilot, Cortex Code is your upgrade path.

It enables complex tasks in data engineering, analytics, machine learning, and agent development through natural language. What sets it apart is its deep understanding of Snowflake's data, compute, governance, and operations.

Available in two forms:

Pros:

• Code generation with awareness of your Snowflake data context
• Accelerates data pipeline, analytics, and AI app development
• Enterprise-grade security and governance
• Integrates into existing development workflows (local IDE)

Cons / Considerations:

• This is a developer-oriented tool - generated code should be reviewed by technical users before use
• Cortex Code CLI requires environment setup

Best Use Cases:

• SQL query authoring and optimization
• Data pipeline construction
• Cortex Agents development and tuning
• Day-to-day data development task acceleration

Vercel v0 Integration

⚠️ Note: Vercel v0 integration was announced in November 2025 but is still under development as of this article (February 2026) and is not yet available to general users. The information below is based on the announcement and specifications may change at GA.

Overview:
The Vercel v0 integration, announced in November 2025, will enable generating and deploying Next.js applications powered by Snowflake data using natural language. v0 is Vercel's AI assistant for full-stack application development. Generated apps are deployed on SPCS.

SiS vs. Vercel v0 Integration:

SiS is ideal for data analysts and scientists who want to quickly turn their analyses into interactive apps using Python. Vercel v0 integration is for frontend developers who want to build production-quality native web apps from the start.

Capabilities:

• Ask questions about data: Query schemas, table structures, and data content in natural language
• Generate applications: Create Next.js apps for data visualization, dashboards, internal tools, etc.
• Deploy to Snowflake: Deploy generated apps on SPCS

Architecture:
Vercel's "Secure Vibe Coding Architecture" ensures application and auth layers are managed by Vercel while compute and data stay within Snowflake. Data never leaves Snowflake, and existing security policies and access controls apply.

Example Use Cases:

• Sales pipeline dashboards
• Inventory monitoring tools
• Customer analytics applications
• Financial reporting interfaces

Official blog: Build and deploy data applications on Snowflake with v0

AI Agent / MCP Integration

Interfaces for accessing Snowflake data and capabilities from AI Agents.

Who should consider MCP? If you're already using AI Agents like Claude Desktop, Cursor, or GitHub Copilot and want to add Snowflake to your AI ecosystem, this is for you. MCP (Model Context Protocol) - often called the "USB-C for AI" - provides unified access to Snowflake data from multiple AI tools with a single configuration.

Key Value of MCP Integration:

Snowflake Managed MCP Server

Overview:
Snowflake Managed MCP Server is an MCP server hosted and managed by Snowflake. Configure it from Snowsight and connect MCP Clients like Claude Desktop or Cursor to Snowflake capabilities. GA since November 2025.

Pros:

• Easy setup (configure from Snowsight, ~15-25 minutes)
• Operates within Snowflake's security model
• Seamless integration with Cortex features (Analyst / Search / Agents)
• Officially supported, no infrastructure management required

Cons / Considerations:

• Requires separate MCP Client setup

Best Use Cases:

• Access Snowflake data from Claude Desktop / Cursor and other MCP Clients
• Automated data analysis via AI Agents
• RAG application backends

OSS Snowflake MCP Server

Overview:
An open-source MCP Server published by Snowflake Labs on GitHub. Runs locally and connects with MCP Clients.

Pros:

• Fine-grained SQL execution permissions (e.g., allow SELECT only)
• Flexible customization in local environments
• Early access to the latest features

Cons / Considerations:

• Requires local setup and management
• Community support (not officially supported)

Best Use Cases:

• Snowflake data analysis in development environments
• Ad-hoc analysis from IDEs like Cursor
• AI Agent integration PoCs

For a deep dive into using the Snowflake MCP Server with Cursor, check out my previous article: Unlock Advanced Data Analytics in Cursor with Snowflake MCP Server

Programmatic Access

Tools for connecting to Snowflake via command line or code for development and operations.

Snowflake CLI

Overview:
Snowflake CLI is Snowflake's official open-source command-line interface. Positioned as the successor to SnowSQL, it includes SnowSQL's capabilities plus integrated development and deployment features for Snowpark apps, Streamlit apps, Native Apps, and more. Snowflake recommends migrating from SnowSQL to Snowflake CLI.

Pros:

• CI/CD pipeline integration
• Deploy Snowpark, Streamlit, and Native Apps from the command line
• Open source with community-driven extensions
• Usable from SSH-connected servers

Cons / Considerations:

• No GUI - not ideal for visual data exploration
• Some command syntax differences when migrating from SnowSQL

If you're currently using SnowSQL, refer to the official migration guide to plan your transition to Snowflake CLI.

Snowpark Python SDK

Overview:
A Python SDK for natively interacting with Snowflake. Provides intuitive data manipulation through the DataFrame API.

Pros:

• pandas-like interface
• Performance gains through Snowflake-side pushdown execution
• Snowpark ML integration for machine learning workflows

Cons / Considerations:

• Some advanced SQL features require direct SQL

SQL API and Connectors

Overview:
Snowflake provides a REST API and drivers/connectors for various programming languages, enabling applications to connect to Snowflake for data operations.

SQL REST API

The Snowflake SQL REST API (/api/v2/statements) enables SQL execution via HTTP requests.

Authentication supports OAuth and JWT (key-pair authentication).

Drivers / Connectors

A comprehensive set of drivers and connectors for various programming languages:

REST API vs. Connectors:

Pros:

• Easy integration into existing applications
• Fits microservice architectures well
• Broad programming language support
• REST API works in serverless environments (AWS Lambda, etc.)

Cons / Considerations:

• REST API is best for lightweight operations; connectors recommended for high-volume data processing

Third-Party Integration

Methods for connecting to Snowflake from external tools and applications.

BI Tools

Overview:
Connect to Snowflake from existing BI tools like Tableau, Power BI, Looker, Sigma, ThoughtSpot, and others for visualization.

Pros:

• Extensive visualization capabilities
• Leverage existing BI skills and expertise
• Integrate with tools already in your organization
• Enterprise-grade features (scheduling, permissions, distribution, etc.)

Cons / Considerations:

• Additional BI tool licensing costs
• Data freshness concerns with extract mode
• Some tools may lag in supporting latest Snowflake features

Best Use Cases:

• Executive dashboards
• Scheduled report distribution
• Complex visualizations
• Large-scale BI deployments

Custom Applications

Overview:
Build custom applications using Snowflake Connectors or SQL API.

Pros:

• Full control over UI/UX
• Deep integration with existing systems
• Custom business logic implementation

Cons / Considerations:

• Security design is your responsibility
• Authentication and authorization must be implemented

Use Case-Based Selection Guide

For those asking "So which one should I actually use?" - here's a practical selection guide.

Recommended Tools by User Type

Recommended Tools by Use Case

Cortex Code AI Assistance: Workspaces, Notebooks, and SiS all support Cortex Code for AI-assisted coding. Generate and edit code with natural language to dramatically boost development productivity. For local IDEs (VS Code, Cursor, etc.), Cortex Code CLI is also available.

Key Decision Factors

1. SQL / Python skills: Proficient → Workspaces / Notebooks. Not proficient → Intelligence
2. Need dashboards / visualizations? Simple ones → SiS (Container Runtime) at low cost. Advanced → BI tools
3. Customization needs: High → SiS / SPCS / custom development
4. GPU required? → Notebooks (Container Runtime) or SPCS
5. Can data leave Snowflake? No → Snowflake native UI (SiS / SPCS / Notebooks)
6. Existing tools: Already have BI tools → connect them to Snowflake. Already using AI Agents → connect via MCP Server
7. Scale of deployment: Organization-wide → Intelligence. Team-level → Intelligence / SiS
8. AI strategy: Heavy AI Agent use → MCP Server. Development assistance → Cortex Code. App generation → Vercel v0 (coming soon)

Conclusion

Snowflake's data UI has diversified remarkably in just two years. While the abundance of options can feel overwhelming, it also means you can now choose the tool that's truly optimal for your organization and use case.

Personally, I find the most value in:

• Snowflake Intelligence for data democratization: Business users who can't write SQL can now query data directly
• Snowflake Managed MCP Server for AI Agent integration: Analyze data without leaving your development tools
• SiS simplicity with Container Runtime: Build custom dashboards and analytics tools in Python alone, with Container Runtime enabling low-cost always-on operation
• Cortex Code / Vercel v0 integration potential: AI-assisted development dramatically accelerating data and app development

The analytics experience is evolving from static reports to interactive conversations with data, gaining insights alongside AI. I hope this guide helps you find the right tools to accelerate your data journey!

Promotion

Snowflake What's New Updates on X

I share Snowflake What's New updates on X. Follow for the latest insights:

English Version

Snowflake What's New Bot (English Version)

Japanese Version

Snowflake's What's New Bot (Japanese Version)

Change Log

(20260215) Initial post

Original Japanese Article

https://zenn.dev/snowflakejp/articles/be0c2053116787