How to use Snowflake with Python in AWS Lambda

Snowflake is a powerful data warehouse platform that allows you to store, analyze, and query large amounts of structured and semi-structured data. AWS Lambda, on the other hand, is a serverless compute service that lets you run your code without provisioning or managing servers. In this article, we will explore how to use Snowflake with Python in AWS Lambda.

Overview

To interact with Snowflake from a Lambda function, we need to manage the integration using Python code. AWS Lambda does not provide built-in support for Snowflake, so we will configure Snowflake resources such as a Pipe and a Warehouse, along with AWS resources like aws.lambda.Function. We will write a Python-based Lambda function that connects to Snowflake and runs the necessary queries.

Setting up the Environment

Before we dive into the code, we need to ensure we have the necessary dependencies and access privileges.

Dependencies

To communicate with Snowflake from a Lambda function, we need to install the snowflake-connector-python library. We can include this library in our Lambda function package. Additionally, we need to install the necessary dependencies for the Python code that will interact with Snowflake, such as the pandas and numpy libraries for data manipulation and analysis.

Access Privileges

To connect to Snowflake from your Lambda function, you will need to provide appropriate credentials. Snowflake provides different authentication methods, such as username/password, single sign-on (SSO), and multi-factor authentication (MFA). You need to ensure that you have the required credentials and access privileges to connect to Snowflake.

Writing the Lambda Function

Now that we have the necessary environment set up, we can proceed to write our Lambda function code. In this example, we will assume that you have a Python function that interacts with Snowflake to retrieve data.

Step 1: Importing Dependencies

First, we need to import the necessary libraries and modules in our Lambda function code. We need to import the snowflake.connector library to establish a connection with Snowflake and execute queries. Additionally, we may need to import other libraries such as pandas and numpy if we plan to manipulate and analyze data in our function.

import snowflake.connector
import pandas as pd
import numpy as np

Step 2: Connecting to Snowflake

Next, we need to establish a connection to Snowflake using the provided credentials. We can create a function that takes the necessary connection parameters (e.g., account, username, password) as input and returns a Snowflake connection object.

def connect_to_snowflake(account, username, password):
    connection = snowflake.connector.connect(
        account=account,
        user=username,
        password=password
    )
    return connection

Step 3: Executing Queries

Once we have established the connection, we can execute queries against Snowflake. We can create a function that takes the Snowflake connection object and a SQL query as input, and returns the query result as a pandas DataFrame.

def execute_query(connection, query):
    cursor = connection.cursor()
    cursor.execute(query)
    result = cursor.fetchall()
    columns = [column[0] for column in cursor.description]
    df = pd.DataFrame(result, columns=columns)
    return df

Step 4: Lambda Handler Function

Now, we can define our Lambda handler function that will be executed when the Lambda function is invoked. This function will be the entry point for our code.

def lambda_handler(event, context):
    # Retrieve the necessary Snowflake connection parameters from the event
    account = event["account"]
    username = event["username"]
    password = event["password"]

    # Connect to Snowflake
    connection = connect_to_snowflake(account, username, password)

    # Execute a sample query
    query = "SELECT * FROM my_table"
    result = execute_query(connection, query)

    # Process the query result
    # (e.g., perform data analysis, transformation, etc.)

    # Return the processed result
    return result.to_dict()

In this code snippet, we assume that the Snowflake connection parameters are passed to the Lambda function as part of the event object. You can modify this code to suit your specific needs, such as retrieving the connection parameters from environment variables.

Step 5: Packaging the Lambda Function

To deploy our Lambda function, we need to package it along with the necessary dependencies. We can create a zip file that includes our Lambda function code (lambda_api_handler.py) and the required libraries (snowflake-connector-python, pandas, numpy). We can use the pulumi.AssetArchive function to package the code:

import pulumi

package = pulumi.AssetArchive({
    ".": pulumi.FileArchive("./lambda_handler_dir")
})

Step 6: Deploying the Lambda Function

To deploy the Lambda function, we can use the aws.lambda.Function resource from the pulumi_aws package. We need to provide the packaged Lambda function code, the IAM role for the Lambda function, and other necessary configuration options.

import pulumi_aws as aws

lambda_function = aws.lambda_.Function(
    "lambdaFunction",
    code=package,
    role=lambda_role.arn,
    handler="lambda_handler.lambda_handler",
    runtime="python3.8"
)

Make sure to replace lambda_role.arn with the ARN of the IAM role for your Lambda function.

Creating Snowflake Resources

In addition to writing the Lambda function, we also need to set up Snowflake resources such as a Pipe and a Warehouse. These resources can be created using the pulumi_snowflake package. Here’s an example of how to create a Snowflake Pipe and Warehouse:

import pulumi_snowflake as snowflake

warehouse = snowflake.warehouse.Warehouse(
    "warehouse",
    name="my_warehouse",
    warehouse_size="X-Small"
)

pipe = snowflake.pipe.Pipe(
    "pipe",
    auto_ingest=False,
    database="<database>",
    copy_statement="COPY INTO <table>",
    name="my_pipe",
    schema="<schema>"
)

Please replace <database>, <table>, and <schema> with the actual values you want to use.

Conclusion

In this article, we have explored how to use Snowflake with Python in AWS Lambda. We have written a Lambda function that connects to Snowflake, executes queries, and processes the results. We have also created Snowflake resources such as a Pipe and a Warehouse using the pulumi_snowflake package. By integrating Snowflake and AWS Lambda, you can leverage the power of Snowflake’s data warehousing capabilities in a serverless environment.

Remember to handle security considerations, such as encryption, access control, and data privacy, when working with sensitive data in Snowflake and AWS Lambda. Always follow best practices and consult the official documentation for Snowflake and AWS Lambda for more information.