README.md

faker_to_snowflake

Purpose

1. To create a sizable amount of fake data in Snowflake in the absolute cheapest way possible.
2. Showing cost difference between write_pandas (copy command) vs Snowpipe.
   • snowpipe does not use a warehouse; it ends up being significantly cheaper.

Getting Started

If running locally

1. Install pdm globally, if you don't already have it. pip install pdm
2. run pdm install, which will install of the dependancies for this project.

.devcontainer and instructions will be added soon!

Further steps regardless of Local vs devcontainer

1. Prerequisite: aws cli configured and working.
2. Add a .env file to the project root and add these variables.

   SNOWFLAKE_USER=YOUR_USER
   SNOWFLAKE_PASSWORD=YOUR_PASSWORD
   SNOWFLAKE_ACCOUNT=your-account
   SNOWFLAKE_WAREHOUSE=YOUR_WAREHOSUE
   SNOWFLAKE_DATABASE=YOUR_DATABASE
   SNOWFLAKE_SCHEMA=YOUR_SCHEMA
   SNOWFLAKE_ROLE=YOUR_ROLE
   SECRET_NAME = your-snowflake-secret-name-in-secrets-manager
   REGION = aws-region-of-your-secret
   

3. Review config.py to see how variables are set from .env
4. Directly run SecretsManager.py once. This will create a new secret in AWS Secrets Manager according to your environment variables set in .env.
   • This assumes you have rsa_key.p8 in the root directory of the project. This is the key file for your Snowflake user.
   • This project does not cover setting up the keypair auth.
5. You can run this locally by running fake_data_to_snowflake.py directly, adjusting the number_of_rows input.

Deploying this as AWS Lambda

• I'm hoping to add automated steps using aws cdk in the near future. For now, this is not a "one click build" project.
• For now, follow these manual steps:

1. Build the docker container.
2. Push it to ECR
3. Create a lambda that runs docker, select an arm64 image type.
4. json body should be like this:

   {
   "rows": 100000
   }
   

5. Schedule it using EventBridge.
6. Enjoy your fake streaming table.