Skip to content
BranchesTags

Repository files navigation

Snowflake HackDays 2026 — Demos & Learning Hub

A monorepo containing production-grade Snowflake applications, interactive Streamlit dashboards, machine learning pipelines, and hands-on SQL/Python tutorials powered by Snowflake Cortex AI, Snowpark Python, and free public datasets.

🧭 Monorepo Projects Overview

hd2026-demos/
├── llm-chat-lab/                 # Cortex LLM Chat Assistant + FEMA Flood Insurance ML Model
├── retail-intelligence/          # Retail BI Dashboard, Inventory Monitor & AI Business Advisor
├── us_macroeconomics_dashboard/  # US Macroeconomic & Corporate Intelligence Observatory
└── public_free_data_access/      # Progressive SQL, Python & Jupyter tutorials on public datasets
Project Key Technologies Description Setup Time
LLM Chat Lab Snowflake Cortex (llama3.1-70b), Streamlit, Scikit-learn, Snowpark Real-time multi-turn LLM chat interface + Random Forest classifier predicting flood damage causes on 2.7M+ FEMA claims. ~2 mins
Retail Intelligence Streamlit, Snowflake SQL Views, Cortex AI Advisor, Pandas Full-stack retail business intelligence platform with revenue analytics, customer lifetime value, inventory health, and AI recommendations. ~3 mins
US Macro Observatory Streamlit (SPCS), Cortex AI, Scikit-learn, Plotly, Public Data 11-page macroeconomic observatory with 50-state unemployment, 7-category CPI, FHFA housing indices, 2M+ SEC company directory, and ML forecasting. ~5 mins
Public Free Data Access SQL (Beginner to Advanced), Python (Pandas/Seaborn), Jupyter Notebooks Structured hands-on labs and tutorials utilizing SNOWFLAKE_PUBLIC_DATA_FREE and SNOWFLAKE_SAMPLE_DATA. Instant

📋 Table of Contents

  1. Global Prerequisites
  2. Step-by-Step Global Snowflake Setup
  3. Quick Start & Setup by Project
  4. Deployment Environments
  5. Architecture & Tech Stack
  6. Global Troubleshooting Matrix

Global Prerequisites

Before starting, ensure you have:

  • A Snowflake Account with ACCOUNTADMIN role access (Free Trial accounts at signup.snowflake.com).
  • Python 3.10, 3.11, or 3.12+ installed on your local machine (if developing locally).
  • Git installed to clone the repository.
  • A virtual warehouse named COMPUTE_WH in Snowflake.

Step-by-Step Global Snowflake Setup

Step 1: Mount Free Public Data from Marketplace

All projects in this repository utilize free public data provided by Snowflake.

  1. Sign in to Snowsight.
  2. Go to Data Products > Marketplace.
  3. Search for "Snowflake Public Data Free" (published by Snowflake).
  4. Click Get and ensure the database name is set to: 
    SNOWFLAKE_PUBLIC_DATA_FREE
  5. Click Get to mount the database into your account.

Step 2: Configure Snowflake Network Policy

When connecting from local machines, IDEs, or using Personal Access Tokens (PAT), Snowflake requires an active Network Policy:

  1. Open a new SQL Worksheet in Snowsight.
  2. Set your role to ACCOUNTADMIN.
  3. Run:
-- Create an open network policy for development
CREATE OR REPLACE NETWORK POLICY ALLOW_ALL_NP
  ALLOWED_IP_LIST = ('0.0.0.0/0');

-- Apply policy to the account
ALTER ACCOUNT SET NETWORK_POLICY = ALLOW_ALL_NP;

Step 3: Setup Databases and Analytical Views

Execute the combined master initialization script in a Snowsight SQL Worksheet:

USE ROLE ACCOUNTADMIN;
USE WAREHOUSE COMPUTE_WH;

-- ============================================================
-- 1. Setup LLM Chat Lab Database & Schema
-- ============================================================
CREATE DATABASE IF NOT EXISTS LLM_CHAT_LAB_DB;
CREATE SCHEMA IF NOT EXISTS LLM_CHAT_LAB_DB.ML;

-- ============================================================
-- 2. Setup Retail Intelligence Database & Tables
-- ============================================================
CREATE DATABASE IF NOT EXISTS RETAIL_INTELLIGENCE_DB;
CREATE SCHEMA IF NOT EXISTS RETAIL_INTELLIGENCE_DB.RETAIL;

USE DATABASE RETAIL_INTELLIGENCE_DB;
USE SCHEMA RETAIL;

CREATE TABLE IF NOT EXISTS CUSTOMERS (
    CUSTOMER_ID     INT PRIMARY KEY,
    CUSTOMER_NAME   VARCHAR(100) NOT NULL,
    EMAIL           VARCHAR(150),
    COUNTRY         VARCHAR(50)  NOT NULL,
    CREATED_AT      DATE
);

CREATE TABLE IF NOT EXISTS PRODUCTS (
    PRODUCT_ID      INT PRIMARY KEY,
    PRODUCT_NAME    VARCHAR(150) NOT NULL,
    CATEGORY        VARCHAR(50)  NOT NULL,
    PRICE           DECIMAL(10, 2) NOT NULL,
    STOCK           INT DEFAULT 0
);

CREATE TABLE IF NOT EXISTS ORDERS (
    ORDER_ID        INT PRIMARY KEY,
    CUSTOMER_ID     INT REFERENCES CUSTOMERS(CUSTOMER_ID),
    PRODUCT_ID      INT REFERENCES PRODUCTS(PRODUCT_ID),
    ORDER_DATE      DATE NOT NULL,
    QUANTITY        INT NOT NULL,
    TOTAL_AMOUNT    DECIMAL(10, 2) NOT NULL
);

-- Seed Retail Data
INSERT INTO CUSTOMERS (CUSTOMER_ID, CUSTOMER_NAME, EMAIL, COUNTRY, CREATED_AT) VALUES
(1,  'Aanya Sharma',    'aanya@example.com',    'India',          '2023-01-10'),
(2,  'James Carter',    'james@example.com',    'United States',  '2023-02-14'),
(3,  'Li Wei',          'liwei@example.com',    'China',          '2023-03-05'),
(4,  'Fatima Al-Said',  'fatima@example.com',   'UAE',            '2023-03-22'),
(5,  'Carlos Rivera',   'carlos@example.com',   'Brazil',         '2023-04-01');

INSERT INTO PRODUCTS (PRODUCT_ID, PRODUCT_NAME, CATEGORY, PRICE, STOCK) VALUES
(1,  'UltraBook Pro 15',                     'Electronics',    1299.99,  45),
(2,  'Wireless Noise Cancelling Headphones', 'Electronics',     249.99, 180),
(3,  'Running Shoes X9',                     'Sports',           89.99, 320),
(4,  'Yoga Mat Premium',                     'Sports',           34.99,  80),
(5,  'Python Programming Guide',             'Books',            29.99, 500);

INSERT INTO ORDERS (ORDER_ID, CUSTOMER_ID, PRODUCT_ID, ORDER_DATE, QUANTITY, TOTAL_AMOUNT) VALUES
(1001, 1, 1, '2024-01-05', 1, 1299.99),
(1002, 2, 2, '2024-01-08', 2,  499.98),
(1003, 3, 5, '2024-01-12', 3,   89.97),
(1004, 4, 1, '2024-01-15', 1, 1299.99),
(1005, 5, 3, '2024-01-20', 2,  179.98);

CREATE OR REPLACE VIEW RETAIL_SALES AS
SELECT o.ORDER_ID, o.ORDER_DATE, o.QUANTITY, o.TOTAL_AMOUNT,
       c.CUSTOMER_ID, c.CUSTOMER_NAME, c.COUNTRY,
       p.PRODUCT_ID, p.PRODUCT_NAME, p.CATEGORY, p.PRICE
FROM ORDERS o
JOIN CUSTOMERS c ON o.CUSTOMER_ID = c.CUSTOMER_ID
JOIN PRODUCTS p  ON o.PRODUCT_ID = p.PRODUCT_ID;

-- ============================================================
-- 3. Setup US Macroeconomics Dashboard Database & Views
-- ============================================================
CREATE DATABASE IF NOT EXISTS HACKDAYS_APP_DB;
CREATE SCHEMA IF NOT EXISTS HACKDAYS_APP_DB.APP_DATA;

USE DATABASE HACKDAYS_APP_DB;
USE SCHEMA APP_DATA;

CREATE TABLE IF NOT EXISTS WATCHLIST (
    ID NUMBER AUTOINCREMENT PRIMARY KEY,
    ENTITY_TYPE VARCHAR(30) NOT NULL,
    ENTITY_NAME VARCHAR(500) NOT NULL,
    TARGET_VALUE FLOAT,
    NOTES VARCHAR(2000),
    CREATED_AT TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP(),
    UPDATED_AT TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()
);

CREATE TABLE IF NOT EXISTS CHAT_HISTORY (
    ID NUMBER AUTOINCREMENT PRIMARY KEY,
    SESSION_ID VARCHAR(100) NOT NULL,
    ROLE VARCHAR(20) NOT NULL,
    CONTENT VARCHAR(16000),
    CREATED_AT TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()
);

CREATE TABLE IF NOT EXISTS INSIGHTS_LOG (
    ID NUMBER AUTOINCREMENT PRIMARY KEY,
    INSIGHT_TYPE VARCHAR(50) NOT NULL,
    INSIGHT_TEXT VARCHAR(8000) NOT NULL,
    DATA_SNAPSHOT VARIANT,
    CREATED_AT TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()
);

Quick Start & Setup by Project

1. LLM Chat Lab Setup

Workspace Execution (1-Click)

  1. In Snowsight, navigate to Projects > Workspaces.
  2. Open llm-chat-lab/streamlit_app.py and click Run.
  3. To train the ML model, open llm-chat-lab/ml/train_flood_claim_model.py and run it as a Python script.

Local Terminal Execution

cd llm-chat-lab
python3 -m venv .venv
source .venv/bin/activate  # Windows: .\.venv\Scripts\Activate.ps1
pip install -r requirements.txt || pip install streamlit snowflake-connector-python snowflake-snowpark-python scikit-learn pandas

# Run Chat App
streamlit run streamlit_app.py

# Run ML Training Script
python ml/train_flood_claim_model.py

2. Retail Intelligence Setup

Workspace Execution (1-Click)

  1. In Snowsight, navigate to Projects > Workspaces.
  2. Open retail-intelligence/app.py and click Run.

Local Terminal Execution

cd retail-intelligence
python3 -m venv .venv
source .venv/bin/activate  # Windows: .\.venv\Scripts\Activate.ps1
pip install -r requirements.txt

# Create .env credentials file
cat <<EOF > .env
SNOWFLAKE_ACCOUNT=your_account_identifier
SNOWFLAKE_USER=your_username
SNOWFLAKE_PASSWORD=your_password
SNOWFLAKE_ROLE=ACCOUNTADMIN
SNOWFLAKE_WAREHOUSE=COMPUTE_WH
SNOWFLAKE_DATABASE=RETAIL_INTELLIGENCE_DB
SNOWFLAKE_SCHEMA=RETAIL
EOF

# Test connection & run
python test_connection.py
streamlit run app.py

3. US Macroeconomics Dashboard Setup

Workspace Execution (1-Click)

  1. In Snowsight, navigate to Projects > Workspaces.
  2. Open us_macroeconomics_dashboard/streamlit_app.py and click Run.

Local Terminal Execution

cd us_macroeconomics_dashboard
python3 -m venv .venv
source .venv/bin/activate  # Windows: .\.venv\Scripts\Activate.ps1
pip install streamlit snowflake-connector-python scikit-learn pandas numpy plotly altair

# Run app
streamlit run streamlit_app.py

4. Public Free Data Tutorials Setup

Running SQL Tutorials

Open any script in public_free_data_access/sql/ inside a Snowsight SQL Worksheet:

  • 01_beginner_exploring_datasets.sql
  • 02_intermediate_single_dataset.sql
  • 03_advanced_cross_dataset.sql
  • 04_tpch_retail_training.sql

Running Python Scripts & Notebooks

cd public_free_data_access
pip install pandas numpy matplotlib seaborn jupyterlab

# Run standalone analysis
python python/01_beginner_python.py
python python/02_intermediate_python.py
python python/03_advanced_python.py

# Launch interactive notebooks
jupyter lab notebooks/

Deployment Environments

Snowflake Workspaces (Zero-Config)

Snowflake Workspaces provides an embedded containerized runtime:

  • No credential management: Authenticates via the active user session.
  • Pre-installed connectors: st.connection("snowflake") and Snowpark get_active_session() connect automatically.
  • Compute options: Backed by COMPUTE_WH and SYSTEM_COMPUTE_POOL_CPU.

Local Development Setup

When developing locally:

  1. Always isolate dependencies inside .venv.
  2. Provide connection parameters via .env or .streamlit/secrets.toml.
  3. Account Identifiers format: <org>-<account> or <account_locator> (e.g., xy12345).

Architecture & Tech Stack

┌─────────────────────────────────────────────────────────────┐
│                      PRESENTATION LAYER                     │
│  Streamlit • Plotly • Altair • Native Snowflake Notebooks   │
├─────────────────────────────────────────────────────────────┤
│                     AI & ANALYTICS LAYER                    │
│  Snowflake Cortex (Llama 3.1 70B, Claude 3.5 Sonnet)       │
│  Snowpark Python • Scikit-learn (RandomForest, Regression)  │
├─────────────────────────────────────────────────────────────┤
│                       DATA & STORAGE                        │
│  Snowflake Cloud DW • SNOWFLAKE_PUBLIC_DATA_FREE            │
│  Analytical Views • Dynamic SQL Caching (@st.cache_data)    │
└─────────────────────────────────────────────────────────────┘

Global Troubleshooting Matrix

Issue Cause Solution
Database 'SNOWFLAKE_PUBLIC_DATA_FREE' does not exist Public marketplace listing not mounted Go to Marketplace > search Snowflake Public Data Free > click Get.
Fail: Network policy is required (Error 390432) PAT or local IP blocked by account policy Run CREATE OR REPLACE NETWORK POLICY ALLOW_ALL_NP ALLOWED_IP_LIST = ('0.0.0.0/0'); ALTER ACCOUNT SET NETWORK_POLICY = ALLOW_ALL_NP; using ACCOUNTADMIN.
Database 'RETAIL_INTELLIGENCE_DB' does not exist Setup SQL not executed Run retail-intelligence/sql/setup.sql in Snowsight.
Database 'HACKDAYS_APP_DB' does not exist Setup SQL not executed Run us_macroeconomics_dashboard/sql/01_setup_database.sql and sql/04_analytical_views.sql.
Cortex LLM function execution failed Region does not support target model Change MODEL parameter to mistral-large2 or llama3.1-8b in app settings.

License

MIT License

About

Feel free to check/use/anything with our demos for HackDays Ahmedabad | Sept 26, 2026!

Resources

Readme
MIT license
Activity
Custom properties

Stars

1 star

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages