Snowflake Hello World

Snowflake Hello World

Overview

Minimal working examples demonstrating core Snowflake operations: connect, query, create objects, load data.

Prerequisites

• Completed snowflake-install-auth setup
• Valid credentials configured in environment
• A warehouse available (e.g., COMPUTE_WH)

Instructions

Step 1: Connect and Query (Node.js)

// hello-snowflake.ts
import snowflake from 'snowflake-sdk';

const connection = snowflake.createConnection({
  account: process.env.SNOWFLAKE_ACCOUNT!,
  username: process.env.SNOWFLAKE_USER!,
  password: process.env.SNOWFLAKE_PASSWORD!,
  warehouse: 'COMPUTE_WH',
  database: 'DEMO_DB',
  schema: 'PUBLIC',
});

connection.connect((err) => {
  if (err) {
    console.error('Connection failed:', err.message);
    process.exit(1);
  }
  console.log('Connected to Snowflake!');

  // Run a simple query
  connection.execute({
    sqlText: `SELECT CURRENT_TIMESTAMP() AS now,
              CURRENT_WAREHOUSE() AS warehouse,
              CURRENT_DATABASE() AS database,
              CURRENT_ROLE() AS role`,
    complete: (err, stmt, rows) => {
      if (err) {
        console.error('Query failed:', err.message);
        return;
      }
      console.log('Query result:', rows);
      connection.destroy((err) => {
        if (err) console.error('Disconnect error:', err.message);
      });
    },
  });
});

Step 2: Connect and Query (Python)

# hello_snowflake.py
import snowflake.connector
import os

conn = snowflake.connector.connect(
    account=os.environ['SNOWFLAKE_ACCOUNT'],
    user=os.environ['SNOWFLAKE_USER'],
    password=os.environ['SNOWFLAKE_PASSWORD'],
    warehouse='COMPUTE_WH',
    database='DEMO_DB',
    schema='PUBLIC',
)

try:
    cursor = conn.cursor()
    cursor.execute("""
        SELECT CURRENT_TIMESTAMP() AS now,
               CURRENT_WAREHOUSE() AS warehouse,
               CURRENT_DATABASE() AS database,
               CURRENT_ROLE() AS role
    """)
    for row in cursor:
        print(f"Time: {row[0]}, Warehouse: {row[1]}, DB: {row[2]}, Role: {row[3]}")
finally:
    conn.close()

Step 3: Create Database Objects

-- Run via connection.execute() or snowflake worksheet
CREATE DATABASE IF NOT EXISTS DEMO_DB;
CREATE SCHEMA IF NOT EXISTS DEMO_DB.MY_SCHEMA;

CREATE OR REPLACE TABLE DEMO_DB.MY_SCHEMA.USERS (
    id INTEGER AUTOINCREMENT,
    name VARCHAR(100) NOT NULL,
    email VARCHAR(255),
    created_at TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()
);

INSERT INTO DEMO_DB.MY_SCHEMA.USERS (name, email)
VALUES ('Alice', 'alice@example.com'),
       ('Bob', 'bob@example.com');

SELECT * FROM DEMO_DB.MY_SCHEMA.USERS;

Step 4: Parameterized Queries (Node.js)

// Insert with bind parameters — prevents SQL injection
connection.execute({
  sqlText: 'INSERT INTO DEMO_DB.MY_SCHEMA.USERS (name, email) VALUES (?, ?)',
  binds: ['Charlie', 'charlie@example.com'],
  complete: (err, stmt, rows) => {
    if (err) {
      console.error('Insert failed:', err.message);
      return;
    }
    console.log('Inserted rows:', stmt.getNumUpdatedRows());
  },
});

// Fetch results with streaming for large datasets
connection.execute({
  sqlText: 'SELECT * FROM DEMO_DB.MY_SCHEMA.USERS ORDER BY created_at DESC',
  streamResult: true,
  complete: (err, stmt) => {
    if (err) { console.error(err.message); return; }
    const stream = stmt.streamRows();
    stream.on('data', (row) => console.log('Row:', row));
    stream.on('end', () => console.log('All rows fetched'));
    stream.on('error', (err) => console.error('Stream error:', err));
  },
});

Step 5: Parameterized Queries (Python)

# Insert with bind parameters
cursor.execute(
    "INSERT INTO DEMO_DB.MY_SCHEMA.USERS (name, email) VALUES (%s, %s)",
    ('Charlie', 'charlie@example.com')
)
print(f"Inserted {cursor.rowcount} row(s)")

# Fetch all results
cursor.execute("SELECT * FROM DEMO_DB.MY_SCHEMA.USERS ORDER BY created_at DESC")
results = cursor.fetchall()
for row in results:
    print(f"ID: {row[0]}, Name: {row[1]}, Email: {row[2]}")

# Fetch as pandas DataFrame
import pandas as pd
cursor.execute("SELECT * FROM DEMO_DB.MY_SCHEMA.USERS")
df = cursor.fetch_pandas_all()
print(df)

Output

Connected to Snowflake!
Query result: [{ NOW: '2026-03-22T...', WAREHOUSE: 'COMPUTE_WH', DATABASE: 'DEMO_DB', ROLE: 'SYSADMIN' }]
Inserted 1 row(s)
Row: { ID: 1, NAME: 'Alice', EMAIL: 'alice@example.com', CREATED_AT: '...' }

Error Handling

Resources

• Executing Statements (Node.js)
• Using the Python Connector
• Snowflake SQL Reference

Next Steps

Proceed to snowflake-local-dev-loop for development workflow setup.

Source: ZenProjectGit/claude-code-plugins-plus-skills — distributed by TomeVault.