Connecting Streamlit To Snowflake

Streamlit Snowflake connection

Connect your Streamlit app to Snowflake the right way.

Use st.connection

Always use st.connection("snowflake") instead of raw connectors.

import streamlit as st

conn = st.connection("snowflake")

# Query data
df = conn.query("SELECT * FROM my_table LIMIT 100")
st.dataframe(df)

Why st.connection:

• Automatic connection pooling
• Built-in caching
• Handles reconnection
• Works with st.secrets

Caller's rights connection (Streamlit 1.53+)

For apps running in Snowflake, use caller's rights to run queries with the viewer's permissions instead of the app owner's:

conn = st.connection("snowflake", type="snowflake-callers-rights")

This is useful when:

• Different users should see different data based on their Snowflake roles
• You want row-level security to apply based on the viewer
• You don't want the app to have elevated permissions

Cached queries

Use the built-in ttl parameter to cache query results:

from datetime import timedelta

conn = st.connection("snowflake")

# Cache for 10 minutes
df = conn.query("SELECT * FROM metrics", ttl=timedelta(minutes=10))

# Cache for 1 hour
df = conn.query("SELECT * FROM reference_data", ttl=3600)

Configure with st.secrets

Store credentials in .streamlit/secrets.toml (never commit this file).

# .streamlit/secrets.toml
[connections.snowflake]
account = "your_account"
user = "your_user"
password = "your_password"
warehouse = "your_warehouse"
database = "your_database"
schema = "your_schema"

Add to .gitignore:

.streamlit/secrets.toml

Parameterized queries

Use parameters to prevent SQL injection:

conn = st.connection("snowflake")

# Safe: parameterized
df = conn.query(
    "SELECT * FROM users WHERE region = :region",
    params={"region": selected_region}
)

# UNSAFE: string formatting - don't do this
# df = conn.query(f"SELECT * FROM users WHERE region = '{selected_region}'")

Write data

Use the session for write operations:

conn = st.connection("snowflake")
session = conn.session()

# Write a dataframe
session.write_pandas(df, "MY_TABLE", auto_create_table=True)

# Execute statements
session.sql("INSERT INTO logs VALUES (:ts, :msg)", params={...}).collect()

Multiple connections

Define multiple connections in secrets:

# .streamlit/secrets.toml
[connections.snowflake]
account = "prod_account"
# ... prod credentials

[connections.snowflake_staging]
account = "staging_account"
# ... staging credentials

prod_conn = st.connection("snowflake")
staging_conn = st.connection("snowflake_staging")

Chat with Cortex

Build a chat interface using Snowflake Cortex LLMs:

import streamlit as st
from snowflake.cortex import complete

st.set_page_config(page_title="AI Assistant", page_icon=":sparkles:")

if "messages" not in st.session_state:
    st.session_state.messages = []

for msg in st.session_state.messages:
    with st.chat_message(msg["role"]):
        st.write(msg["content"])

if prompt := st.chat_input("Ask anything"):
    st.session_state.messages.append({"role": "user", "content": prompt})

    with st.chat_message("user"):
        st.write(prompt)

    with st.chat_message("assistant"):
        response = st.write_stream(
            complete(
                "claude-3-5-sonnet",
                prompt,
                session=st.connection("snowflake").session(),
                stream=True,
            )
        )

    st.session_state.messages.append({"role": "assistant", "content": response})

See building-streamlit-chat-ui for more chat patterns (avatars, suggestions, history management).

References

• st.connection
• SnowflakeConnection
• st.secrets