The prefect-snowflake integration makes it easy to connect to Snowflake in your Prefect flows. You can run queries both synchronously and asynchronously as Prefect flows and tasks.

​
Getting started

​
Prerequisites

​
Installation

Install prefect-snowflake as a dependency of Prefect. If you don’t already have Prefect installed, it will install the newest version of prefect as well.

pip install "prefect[snowflake]"

Upgrade to the latest versions of prefect and prefect-snowflake:

pip install -U "prefect[snowflake]"

​
Blocks setup

The prefect-snowflake integration has two blocks: one for storing credentials and one for storing connection information. Register blocks in this module to view and edit them on Prefect Cloud:

prefect block register -m prefect_snowflake

​
Create the credentials block

Below is a walkthrough on saving a SnowflakeCredentials block through code. Log into your Snowflake account to find your credentials. The example below uses a user and password combination, but refer to the SDK documentation for a full list of authentication and connection options.

from prefect_snowflake import SnowflakeCredentials

credentials = SnowflakeCredentials(
    account="ACCOUNT-PLACEHOLDER",  # resembles nh12345.us-east-2.snowflake
    user="USER-PLACEHOLDER",
    password="PASSWORD-PLACEHOLDER"
)
credentials.save("CREDENTIALS-BLOCK-NAME-PLACEHOLDER")

​
Create the connection block

Then, to create a SnowflakeConnector block:

  1. After logging in, click on any worksheet.
  2. On the left side, select a database and schema.
  3. On the top right, select a warehouse.
  4. Create a short script, replacing the placeholders below.
from prefect_snowflake import SnowflakeCredentials, SnowflakeConnector

credentials = SnowflakeCredentials.load("CREDENTIALS-BLOCK-NAME-PLACEHOLDER")

connector = SnowflakeConnector(
    credentials=credentials,
    database="DATABASE-PLACEHOLDER",
    schema="SCHEMA-PLACEHOLDER",
    warehouse="COMPUTE_WH",
)
connector.save("CONNECTOR-BLOCK-NAME-PLACEHOLDER")

You can now easily load the saved block, which holds your credentials and connection info:

from prefect_snowflake import SnowflakeCredentials, SnowflakeConnector

SnowflakeConnector.load("CONNECTOR-BLOCK-NAME-PLACEHOLDER")

​
Examples

To set up a table, use the execute and execute_many methods. Then, use the fetch_all method. If the results are too large to fit into memory, use the fetch_many method to retrieve data in chunks.

By using the SnowflakeConnector as a context manager, you can make sure that the Snowflake connection and cursors are closed properly after you’re done with them.

from prefect import flow, task
from prefect_snowflake import SnowflakeConnector


@task
def setup_table(block_name: str) -> None:
    with SnowflakeConnector.load(block_name) as connector:
        connector.execute(
            "CREATE TABLE IF NOT EXISTS customers (name varchar, address varchar);"
        )
        connector.execute_many(
            "INSERT INTO customers (name, address) VALUES (%(name)s, %(address)s);",
            seq_of_parameters=[
                {"name": "Ford", "address": "Highway 42"},
                {"name": "Unknown", "address": "Space"},
                {"name": "Me", "address": "Myway 88"},
            ],
        )

@task
def fetch_data(block_name: str) -> list:
    all_rows = []
    with SnowflakeConnector.load(block_name) as connector:
        while True:
            # Repeated fetch* calls using the same operation will
            # skip re-executing and instead return the next set of results
            new_rows = connector.fetch_many("SELECT * FROM customers", size=2)
            if len(new_rows) == 0:
                break
            all_rows.append(new_rows)
    return all_rows

@flow
def snowflake_flow(block_name: str) -> list:
    setup_table(block_name)
    all_rows = fetch_data(block_name)
    return all_rows


if __name__=="__main__":
    snowflake_flow()

If the native methods of the block don’t meet your requirements, don’t worry. You have the option to access the underlying Snowflake connection and utilize its built-in methods as well.

import pandas as pd
from prefect import flow
from prefect_snowflake.database import SnowflakeConnector
from snowflake.connector.pandas_tools import write_pandas


@flow
def snowflake_write_pandas_flow():
    connector = SnowflakeConnector.load("my-block")
    with connector.get_connection() as connection:
        table_name = "TABLE_NAME"
        ddl = "NAME STRING, NUMBER INT"
        statement = f'CREATE TABLE IF NOT EXISTS {table_name} ({ddl})'
        with connection.cursor() as cursor:
            cursor.execute(statement)

        # case sensitivity matters here!
        df = pd.DataFrame([('Marvin', 42), ('Ford', 88)], columns=['NAME', 'NUMBER'])
        success, num_chunks, num_rows, _ = write_pandas(
            conn=connection,
            df=df,
            table_name=table_name,
            database=snowflake_connector.database,
            schema=snowflake_connector.schema_  # note the "_" suffix
        )

​
Resources

Refer to the prefect-snowflake SDK documentation to explore other capabilities of the prefect-snowflake library, such as async methods.

For further assistance using Snowflake, consult the Snowflake documentation or the Snowflake Python Connector documentation.

The prefect-snowflake integration makes it easy to connect to Snowflake in your Prefect flows. You can run queries both synchronously and asynchronously as Prefect flows and tasks.

​
Getting started

​
Prerequisites

​
Installation

Install prefect-snowflake as a dependency of Prefect. If you don’t already have Prefect installed, it will install the newest version of prefect as well.

pip install "prefect[snowflake]"

Upgrade to the latest versions of prefect and prefect-snowflake:

pip install -U "prefect[snowflake]"

​
Blocks setup

The prefect-snowflake integration has two blocks: one for storing credentials and one for storing connection information. Register blocks in this module to view and edit them on Prefect Cloud:

prefect block register -m prefect_snowflake

​
Create the credentials block

Below is a walkthrough on saving a SnowflakeCredentials block through code. Log into your Snowflake account to find your credentials. The example below uses a user and password combination, but refer to the SDK documentation for a full list of authentication and connection options.

from prefect_snowflake import SnowflakeCredentials

credentials = SnowflakeCredentials(
    account="ACCOUNT-PLACEHOLDER",  # resembles nh12345.us-east-2.snowflake
    user="USER-PLACEHOLDER",
    password="PASSWORD-PLACEHOLDER"
)
credentials.save("CREDENTIALS-BLOCK-NAME-PLACEHOLDER")

​
Create the connection block

Then, to create a SnowflakeConnector block:

  1. After logging in, click on any worksheet.
  2. On the left side, select a database and schema.
  3. On the top right, select a warehouse.
  4. Create a short script, replacing the placeholders below.
from prefect_snowflake import SnowflakeCredentials, SnowflakeConnector

credentials = SnowflakeCredentials.load("CREDENTIALS-BLOCK-NAME-PLACEHOLDER")

connector = SnowflakeConnector(
    credentials=credentials,
    database="DATABASE-PLACEHOLDER",
    schema="SCHEMA-PLACEHOLDER",
    warehouse="COMPUTE_WH",
)
connector.save("CONNECTOR-BLOCK-NAME-PLACEHOLDER")

You can now easily load the saved block, which holds your credentials and connection info:

from prefect_snowflake import SnowflakeCredentials, SnowflakeConnector

SnowflakeConnector.load("CONNECTOR-BLOCK-NAME-PLACEHOLDER")

​
Examples

To set up a table, use the execute and execute_many methods. Then, use the fetch_all method. If the results are too large to fit into memory, use the fetch_many method to retrieve data in chunks.

By using the SnowflakeConnector as a context manager, you can make sure that the Snowflake connection and cursors are closed properly after you’re done with them.

from prefect import flow, task
from prefect_snowflake import SnowflakeConnector


@task
def setup_table(block_name: str) -> None:
    with SnowflakeConnector.load(block_name) as connector:
        connector.execute(
            "CREATE TABLE IF NOT EXISTS customers (name varchar, address varchar);"
        )
        connector.execute_many(
            "INSERT INTO customers (name, address) VALUES (%(name)s, %(address)s);",
            seq_of_parameters=[
                {"name": "Ford", "address": "Highway 42"},
                {"name": "Unknown", "address": "Space"},
                {"name": "Me", "address": "Myway 88"},
            ],
        )

@task
def fetch_data(block_name: str) -> list:
    all_rows = []
    with SnowflakeConnector.load(block_name) as connector:
        while True:
            # Repeated fetch* calls using the same operation will
            # skip re-executing and instead return the next set of results
            new_rows = connector.fetch_many("SELECT * FROM customers", size=2)
            if len(new_rows) == 0:
                break
            all_rows.append(new_rows)
    return all_rows

@flow
def snowflake_flow(block_name: str) -> list:
    setup_table(block_name)
    all_rows = fetch_data(block_name)
    return all_rows


if __name__=="__main__":
    snowflake_flow()

If the native methods of the block don’t meet your requirements, don’t worry. You have the option to access the underlying Snowflake connection and utilize its built-in methods as well.

import pandas as pd
from prefect import flow
from prefect_snowflake.database import SnowflakeConnector
from snowflake.connector.pandas_tools import write_pandas


@flow
def snowflake_write_pandas_flow():
    connector = SnowflakeConnector.load("my-block")
    with connector.get_connection() as connection:
        table_name = "TABLE_NAME"
        ddl = "NAME STRING, NUMBER INT"
        statement = f'CREATE TABLE IF NOT EXISTS {table_name} ({ddl})'
        with connection.cursor() as cursor:
            cursor.execute(statement)

        # case sensitivity matters here!
        df = pd.DataFrame([('Marvin', 42), ('Ford', 88)], columns=['NAME', 'NUMBER'])
        success, num_chunks, num_rows, _ = write_pandas(
            conn=connection,
            df=df,
            table_name=table_name,
            database=snowflake_connector.database,
            schema=snowflake_connector.schema_  # note the "_" suffix
        )

​
Resources

Refer to the prefect-snowflake SDK documentation to explore other capabilities of the prefect-snowflake library, such as async methods.

For further assistance using Snowflake, consult the Snowflake documentation or the Snowflake Python Connector documentation.