Daita Documentation

#Installation

bash

pip install snowflake-connector-python

#Quick Start

python

from daita import Agent
from daita.plugins import snowflake
 
# Create plugin
db = snowflake(
    account="xy12345",
    warehouse="COMPUTE_WH",
    database="ANALYTICS_DB",
    user="analyst",
    password="secure_password"
)
 
# Create agent
agent = Agent(
    name="Data Warehouse Analyst",
    model="gpt-4o-mini",
    prompt="You are a data analyst. Help users query and analyze data warehouse data."
)
 
# Give agent access to Snowflake tools
agent.add_plugin(db)
 
await agent.start()
result = await agent.run("What were our top 10 products by revenue last quarter?")

#Direct Usage

The plugin can be used directly without agents for programmatic access. For comprehensive Snowflake documentation, see the official Snowflake docs. The main value of this plugin is agent integration - enabling LLMs to autonomously query and analyze data warehouse data.

#Connection Parameters

python

snowflake(
    account: Optional[str] = None,
    warehouse: Optional[str] = None,
    database: Optional[str] = None,
    schema: str = "PUBLIC",
    user: Optional[str] = None,
    password: Optional[str] = None,
    role: Optional[str] = None,
    private_key_path: Optional[str] = None,
    private_key_passphrase: Optional[str] = None,
    authenticator: Optional[str] = None,
    session_parameters: Optional[Dict] = None
)

#Parameters

account (str): Snowflake account identifier (e.g., "xy12345" or "xy12345.us-east-1")
warehouse (str): Virtual warehouse name for compute resources
database (str): Database name to connect to
schema (str): Schema name (default: "PUBLIC")
user (str): Username for authentication
password (str): Password for password authentication
role (str): Role to use after connecting (optional)
private_key_path (str): Path to RSA private key file for key-pair authentication
private_key_passphrase (str): Passphrase for encrypted private key (optional)
authenticator (str): External authenticator (e.g., "externalbrowser", "oauth")
session_parameters (dict): Additional session parameters

#Environment Variables

All parameters support environment variable fallback:

SNOWFLAKE_ACCOUNT
SNOWFLAKE_WAREHOUSE
SNOWFLAKE_DATABASE
SNOWFLAKE_SCHEMA
SNOWFLAKE_USER
SNOWFLAKE_PASSWORD
SNOWFLAKE_ROLE
SNOWFLAKE_PRIVATE_KEY_PATH
SNOWFLAKE_PRIVATE_KEY_PASSPHRASE
SNOWFLAKE_AUTHENTICATOR

#Authentication Methods

The Snowflake plugin supports three authentication methods. Choose based on your deployment environment and security requirements.

#Authentication Method Comparison

Method	Use Case	Environment	Security	Setup Complexity
Password	Quick testing, development	Local, staging	Basic	Low
Key-Pair	Production, Lambda, CI/CD	Production, cloud	High	Medium
External Browser	Enterprise SSO accounts	Local only	Managed by SSO	Low

#1. Password Authentication

Best for: Local development, testing, personal Snowflake accounts

python

from daita.plugins import snowflake
 
# Explicit credentials
db = snowflake(
    account="xy12345",
    warehouse="COMPUTE_WH",
    database="ANALYTICS",
    user="analyst",
    password="secure_password"
)
 
# Or use environment variables (recommended)
import os
os.environ['SNOWFLAKE_ACCOUNT'] = 'xy12345'
os.environ['SNOWFLAKE_USER'] = 'analyst'
os.environ['SNOWFLAKE_PASSWORD'] = 'secure_password'
 
db = snowflake(
    warehouse="COMPUTE_WH",
    database="ANALYTICS"
)

Pros:

Simple setup
Works everywhere
Familiar authentication flow

Cons:

Less secure than key-pair
Passwords may need rotation
Not recommended for production

#2. Key-Pair Authentication (Recommended for Production)

Best for: Production deployments, Lambda functions, CI/CD pipelines, service accounts

Why use this:

No password to manage or rotate
Works in headless environments (Lambda, Docker, Kubernetes)
More secure than password authentication
Supports encrypted private keys for additional security

Setup:

Generate RSA key pair:

bash

# Generate private key (unencrypted)
openssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt
 
# Generate public key
openssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub

Assign public key to Snowflake user:

sql

ALTER USER service_account SET RSA_PUBLIC_KEY='MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA...';

Use in code:

python

from daita.plugins import snowflake
 
# Unencrypted private key
db = snowflake(
    account="xy12345",
    warehouse="COMPUTE_WH",
    database="ANALYTICS",
    user="service_account",
    private_key_path="/path/to/rsa_key.p8"
)
 
# Or via environment variable
os.environ['SNOWFLAKE_PRIVATE_KEY_PATH'] = '/path/to/rsa_key.p8'
 
db = snowflake(
    account="xy12345",
    warehouse="COMPUTE_WH",
    database="ANALYTICS",
    user="service_account"
)

For encrypted keys:

python

db = snowflake(
    account="xy12345",
    warehouse="COMPUTE_WH",
    database="ANALYTICS",
    user="service_account",
    private_key_path="/path/to/encrypted_rsa_key.p8",
    private_key_passphrase="key_passphrase"
)

Pros:

Most secure authentication method
No password management needed
Works in all environments (including headless)
Industry standard for service accounts

Cons:

Slightly more complex setup
Need to manage private key securely

#3. External Browser Authentication (SSO)

Best for: Enterprise developers with SSO-enabled Snowflake accounts, local development

Important: This method opens a browser window for authentication and only works locally. Cannot be used in Lambda, Docker, or any headless environment.

python

from daita.plugins import snowflake
 
# Browser-based SSO (e.g., Okta, Azure AD)
db = snowflake(
    account="xy12345",
    warehouse="COMPUTE_WH",
    database="ANALYTICS",
    user="analyst@company.com",
    authenticator="externalbrowser"
)
 
# Or via environment variable
os.environ['SNOWFLAKE_AUTHENTICATOR'] = 'externalbrowser'
 
db = snowflake(
    account="xy12345",
    warehouse="COMPUTE_WH",
    database="ANALYTICS",
    user="analyst@company.com"
)

When you run this:

A browser window opens automatically
You authenticate via your company's SSO (Okta, Azure AD, etc.)
After successful login, the connection is established

Pros:

Works with SSO-only Snowflake accounts
Leverages existing enterprise authentication
No password to manage locally
Supports MFA automatically

Cons:

Only works locally - cannot be used in production/Lambda
Requires browser access
Not suitable for automated scripts

Migration path for production: When deploying to production, switch to key-pair authentication:

Local development: Use externalbrowser for easy SSO login
Production/Lambda: Create service account with key-pair auth

#OAuth Authentication

Best for: Advanced integrations with OAuth 2.0 providers

python

db = snowflake(
    account="xy12345",
    warehouse="COMPUTE_WH",
    database="ANALYTICS",
    user="analyst",
    authenticator="oauth",
    session_parameters={"token": "oauth_access_token"}
)

#Query Operations

SELECT queries - Use query() method, returns list of dictionaries:

python

from daita.plugins import snowflake
 
async with snowflake(account="xy12345", warehouse="COMPUTE_WH", database="SALES") as db:
    # Simple query
    customers = await db.query("SELECT id, name, total_spent FROM customers")
    # Returns: [{"id": 1, "name": "Acme Corp", "total_spent": 50000.00}, ...]
 
    # Parameterized query (use %s, %(name)s placeholders)
    high_value = await db.query(
        "SELECT * FROM customers WHERE total_spent > %s AND status = %s",
        [10000, "active"]
    )
 
    # Named parameters
    recent = await db.query(
        "SELECT * FROM orders WHERE order_date >= %(start_date)s",
        {"start_date": "2024-01-01"}
    )

Parameter syntax: Snowflake uses %s for positional parameters and %(name)s for named parameters

#Data Modification

INSERT, UPDATE, DELETE - Use execute() method, returns row count:

python

from daita.plugins import snowflake
 
async with snowflake(account="xy12345", warehouse="LOAD_WH", database="SALES") as db:
    # Insert
    await db.execute(
        "INSERT INTO customers (name, email, status) VALUES (%s, %s, %s)",
        ["New Customer", "customer@example.com", "active"]
    )
 
    # Update
    await db.execute(
        "UPDATE customers SET last_purchase = CURRENT_TIMESTAMP() WHERE id = %s",
        [customer_id]
    )
 
    # Delete
    await db.execute(
        "DELETE FROM customers WHERE status = %s AND days_inactive > %s",
        ["inactive", 365]
    )

#Warehouse Management

Snowflake virtual warehouses control compute resources and costs:

python

from daita.plugins import snowflake
 
async with snowflake(account="xy12345", warehouse="COMPUTE_WH", database="ANALYTICS") as db:
    # List all warehouses
    warehouses = await db.list_warehouses()
    # Returns: [{"name": "COMPUTE_WH", "state": "STARTED", "size": "MEDIUM"}, ...]
 
    # Switch to different warehouse
    await db.switch_warehouse("ANALYTICS_WH")
 
    # Use larger warehouse for heavy queries
    await db.switch_warehouse("LARGE_WH")
    results = await db.query("SELECT * FROM huge_fact_table")
 
    # Switch back to save costs
    await db.switch_warehouse("SMALL_WH")

#Stage Operations

Snowflake stages are used for loading and unloading data:

python

from daita.plugins import snowflake
 
async with snowflake(account="xy12345", warehouse="LOAD_WH", database="RAW_DATA") as db:
    # List available stages
    stages = await db.list_stages()
    # Returns: [{"name": "my_s3_stage", "url": "s3://bucket/path/", "type": "EXTERNAL"}, ...]
 
    # Upload file to stage
    await db.put_file(
        local_path="/data/sales.csv",
        stage_path="@my_stage/sales/",
        overwrite=True
    )
 
    # Download file from stage
    await db.get_file(
        stage_path="@my_stage/sales/sales.csv",
        local_path="/data/downloaded_sales.csv"
    )
 
    # Load data from stage into table
    result = await db.load_from_stage(
        table_name="sales",
        stage_path="@my_stage/sales/",
        file_format="CSV",
        pattern=r".*\.csv"
    )
 
    # Create new stage
    await db.create_stage(
        stage_name="my_new_stage",
        stage_type="internal"
    )
 
    # Create external stage (S3)
    await db.create_stage(
        stage_name="s3_stage",
        stage_type="external",
        url="s3://my-bucket/data/",
        credentials={
            "AWS_KEY_ID": "your_key",
            "AWS_SECRET_KEY": "your_secret"
        }
    )

#Schema Information

python

from daita.plugins import snowflake
 
async with snowflake(account="xy12345", warehouse="COMPUTE_WH", database="ANALYTICS") as db:
    # List all tables
    tables = await db.tables()
    # Returns: ["customers", "orders", "products"]
 
    # List schemas
    schemas = await db.schemas()
    # Returns: ["PUBLIC", "STAGING", "ANALYTICS"]
 
    # Get table structure
    columns = await db.describe("customers")
    # Returns: [{"name": "id", "type": "NUMBER", "nullable": False}, ...]

#Query History

python

from daita.plugins import snowflake
 
async with snowflake(account="xy12345", warehouse="COMPUTE_WH", database="ANALYTICS") as db:
    # Get recent query history
    history = await db.query_history(limit=10)
    # Returns query details including execution time, rows, bytes scanned
 
    # Filter by time range
    from datetime import datetime, timedelta
    start_time = datetime.now() - timedelta(hours=24)
    history = await db.query_history(
        start_time=start_time,
        limit=100
    )

#Using with Agents

#Direct Database Operations (Scripts)

For scripts that don't need agent capabilities:

python

from daita.plugins import snowflake
 
async with snowflake(
    account="xy12345",
    warehouse="ANALYTICS_WH",
    database="SALES",
    user="analyst",
    password="password"
) as db:
    # Analyze sales data
    monthly_sales = await db.query("""
        SELECT
            DATE_TRUNC('month', order_date) as month,
            SUM(total_amount) as revenue
        FROM orders
        WHERE order_date >= DATEADD('year', -1, CURRENT_DATE())
        GROUP BY month
        ORDER BY month DESC
    """)
 
    print(f"Found {len(monthly_sales)} months of data")
    for row in monthly_sales:
        print(f"{row['month']}: ${row['revenue']:,.2f}")

#Tool-Based Integration (Recommended)

Snowflake plugin exposes data warehouse operations as tools that agents can use autonomously:

python

from daita import Agent
from daita.plugins import snowflake
import os
 
# Create Snowflake plugin (auto-loads from environment variables)
db = snowflake(
    account=os.getenv("SNOWFLAKE_ACCOUNT"),
    warehouse="ANALYTICS_WH",
    database="SALES",
    user=os.getenv("SNOWFLAKE_USER"),
    private_key_path=os.getenv("SNOWFLAKE_PRIVATE_KEY_PATH")
)
 
# Create agent
agent = Agent(
    name="Data Warehouse Analyst",
    model="gpt-4o-mini",
    prompt="You are a data analyst specializing in Snowflake data warehouse analysis."
)
 
# Give agent access to Snowflake tools
agent.add_plugin(db)
 
await agent.start()
 
# Agent autonomously uses Snowflake tools to answer questions
result = await agent.run("Analyze our sales trends over the last 6 months")
 
# The agent will autonomously:
# 1. Use list_tables to explore available data
# 2. Use describe_table to understand schema
# 3. Use query_database to fetch sales data
# 4. Analyze trends and present insights
 
await db.disconnect()

#Available Tools

The Snowflake plugin exposes these tools to LLM agents:

Tool	Description	Parameters
query_database	Execute SELECT queries	`sql` (required), `params` (optional array)
execute_sql	Run INSERT/UPDATE/DELETE	`sql` (required), `params` (optional array)
list_tables	List all tables in database	None
list_schemas	List all schemas	None
describe_table	Get table column info	`table_name` (required)
list_warehouses	List available warehouses	None
switch_warehouse	Change active warehouse	`warehouse_name` (required)
get_query_history	Retrieve query history	`limit` (int), `start_time` (datetime)
list_stages	List data stages	None
upload_to_stage	Upload file to stage	`local_path`, `stage_path`, `overwrite` (bool)
download_from_stage	Download file from stage	`stage_path`, `local_path`
load_from_stage	Load stage data into table	`table_name`, `stage_path`, `file_format`, `pattern`

Tool Categories: database, data_warehouse Tool Source: plugin

#Tool Usage Example

python

from daita import Agent
from daita.plugins import snowflake
 
# Create Snowflake plugin
db = snowflake(
    account="xy12345",
    warehouse="ANALYTICS_WH",
    database="ECOMMERCE"
)
 
# Create agent
agent = Agent(
    name="E-commerce Analyst",
    model="gpt-4o-mini",
    prompt="You are an e-commerce analyst. Help users analyze sales, inventory, and customer data."
)
 
# Give agent access to Snowflake tools
agent.add_plugin(db)
 
await agent.start()
 
# Natural language query - agent uses tools autonomously
result = await agent.run("""
Analyze our e-commerce performance:
1. What are the available data tables?
2. Calculate total revenue by product category for Q1 2024
3. Identify top 10 customers by purchase amount
4. Show sales trend over the last 90 days
""")
 
# Agent orchestrates tool calls autonomously:
# - list_tables() to discover schema
# - describe_table("orders") to understand structure
# - query_database(...) to fetch aggregated data
# - Returns comprehensive analysis in natural language
 
print(result)
await db.disconnect()

#Error Handling

python

from daita.plugins import snowflake
 
try:
    async with snowflake(
        account="xy12345",
        warehouse="COMPUTE_WH",
        database="SALES"
    ) as db:
        results = await db.query("SELECT * FROM customers WHERE id = %s", [customer_id])
except RuntimeError as e:
    if "snowflake-connector-python not installed" in str(e):
        print("Install Snowflake connector: pip install snowflake-connector-python")
    elif "authentication" in str(e).lower():
        print(f"Authentication failed: {e}")
    elif "warehouse" in str(e).lower():
        print(f"Warehouse issue: {e}")
    else:
        print(f"Error: {e}")

#Best Practices

Authentication:

Local Development: Use password or externalbrowser (SSO) for convenience
Production/Lambda: Always use key-pair authentication - password and externalbrowser won't work in headless environments
Service Accounts: Create dedicated Snowflake users with key-pair auth for automated systems
Credential Storage: Use environment variables, AWS Secrets Manager, or similar - never hardcode credentials
Key Management: Store private keys securely, use encrypted keys with passphrases in production

Connection Management:

Always use context managers (async with) for automatic cleanup when not using agents
When using agents, call await db.disconnect() at the end of execution
Set appropriate warehouse sizes for your workload
Use connection pooling for high-frequency operations

Warehouse Optimization:

Use smaller warehouses for light queries to reduce costs
Switch to larger warehouses only for heavy analytics
Suspend warehouses when not in use (Snowflake auto-suspend helps)
Monitor query performance and adjust warehouse size accordingly

Query Performance:

Use parameterized queries to prevent SQL injection
Leverage Snowflake's clustering keys for large tables
Use result caching by running identical queries
Limit result sets to avoid memory issues

Data Loading:

Use stages for bulk data loading (more efficient than individual inserts)
Prefer external stages (S3, Azure) for large datasets
Use file patterns to load multiple files at once
Leverage Snowflake's COPY INTO command via load_from_stage()

Security:

Use key-pair authentication for service accounts
Grant minimal required privileges (read-only when possible)
Use separate warehouses for different workloads
Enable multi-factor authentication for user accounts
Regularly rotate credentials

Cost Management:

Monitor warehouse usage and credit consumption
Use auto-suspend and auto-resume features
Right-size warehouses for your workload
Use query history to identify expensive queries

#Troubleshooting

Issue	Solution
`snowflake-connector-python not installed`	`pip install snowflake-connector-python`
Authentication failed (password)	Verify account, username, password are correct
Authentication failed (key-pair)	Check private key format (PEM/DER), verify public key assigned to user in Snowflake
External browser authentication SAML error	Your Snowflake account may not have SSO configured, use password or key-pair auth instead
External browser not opening	Ensure you're running locally (not in Lambda/Docker), check browser is accessible
Authentication required error	Provide at least one auth method: password, private_key_path, or authenticator
Warehouse not found	Check warehouse name, verify it exists with `list_warehouses()`
Database not found	Verify database name, check access permissions
Insufficient privileges	Grant required privileges to user/role
Query timeout	Use larger warehouse, optimize query, check for locks
Network connection issues	Check firewall, verify account region, test network connectivity
Private key format error	Ensure key is in PEM or DER format (PKCS#8), check passphrase
Lambda authentication fails	Use key-pair auth - password and externalbrowser don't work in headless environments

#Next Steps

PostgreSQL Plugin - For PostgreSQL databases
MySQL Plugin - For MySQL databases
Elasticsearch Plugin - For search and analytics
S3 Plugin - For cloud storage integration
Workflows - Use Snowflake in multi-agent workflows
Plugin Overview - Learn about other plugins

Snowflake Plugin

#Installation

#Quick Start

#Direct Usage

#Connection Parameters

#Parameters

#Environment Variables

#Authentication Methods

#Authentication Method Comparison

#1. Password Authentication

#2. Key-Pair Authentication (Recommended for Production)

#3. External Browser Authentication (SSO)

#OAuth Authentication

#Query Operations

#Data Modification

#Warehouse Management

#Stage Operations

#Schema Information

#Query History

#Using with Agents

#Direct Database Operations (Scripts)

#Tool-Based Integration (Recommended)

#Available Tools

#Tool Usage Example

#Error Handling

#Best Practices

#Troubleshooting

#Next Steps