
## Snowflake to S3 Data Transfer with Dagster

### Overview

This use case demonstrates how to transfer data from Snowflake to Amazon S3 using Dagster. The objective is to automate the extraction of data from Snowflake and store it in S3 for further processing or archival. This process is essential for data warehousing, backup, and integration with other data processing tools.

### Prerequisites

Before implementing this use case, ensure you have the following:

1. A Snowflake account with the necessary permissions to read data.
2. An Amazon S3 bucket with appropriate access credentials.
3. A working Dagster installation.
4. Python environment set up with `dagster`, `dagster_snowflake`, and `boto3` libraries installed.

### What You’ll Learn

By following this guide, you will learn how to:

- Set up a Snowflake resource in Dagster.
- Define an asset to extract data from Snowflake.
- Store the extracted data in an S3 bucket using Dagster.

### Steps to Implement With Dagster

1. **Step 1: Set Up Snowflake and S3 Resources**
    - Define the Snowflake and S3 resources in Dagster.
    - Ensure you have the necessary credentials stored securely, for example, using environment variables.

    Example:
    ```python
    from dagster import Definitions, EnvVar
    from dagster_snowflake import SnowflakeResource
    from dagster_aws.s3 import S3Resource

    snowflake_resource = SnowflakeResource(
        account=EnvVar("SNOWFLAKE_ACCOUNT"),
        user=EnvVar("SNOWFLAKE_USER"),
        password=EnvVar("SNOWFLAKE_PASSWORD"),
        warehouse=EnvVar("SNOWFLAKE_WAREHOUSE"),
        database=EnvVar("SNOWFLAKE_DATABASE"),
        role=EnvVar("SNOWFLAKE_ROLE"),
    )

    s3_resource = S3Resource(
        bucket=EnvVar("S3_BUCKET"),
        access_key_id=EnvVar("AWS_ACCESS_KEY_ID"),
        secret_access_key=EnvVar("AWS_SECRET_ACCESS_KEY"),
    )

    defs = Definitions(
        resources={
            "snowflake": snowflake_resource,
            "s3": s3_resource,
        }
    )
    ```

2. **Step 2: Define the Data Extraction Asset**
    - Create an asset that queries data from Snowflake and writes it to a local file or directly to S3.

    Example:
    ```python
    from dagster import asset, AssetExecutionContext
    import pandas as pd

    @asset(required_resource_keys={"snowflake", "s3"})
    def extract_data_from_snowflake(context: AssetExecutionContext):
        query = "SELECT * FROM your_table"
        snowflake = context.resources.snowflake
        s3 = context.resources.s3

        # Execute the query and fetch the data
        df = pd.read_sql(query, snowflake.get_connection())

        # Convert DataFrame to CSV and upload to S3
        csv_buffer = df.to_csv(index=False)
        s3.put_object(Key="data/your_table.csv", Body=csv_buffer)
    ```

3. **Step 3: Materialize the Asset**
    - Use Dagster's `materialize` function to run the asset and transfer data from Snowflake to S3.

    Example:
    ```python
    from dagster import materialize

    materialize([extract_data_from_snowflake], resources={"snowflake": snowflake_resource, "s3": s3_resource})
    ```

### Expected Outcomes

After implementing this use case, you should have:

- Successfully extracted data from Snowflake.
- Stored the extracted data in an S3 bucket in CSV format.
- Automated the data transfer process using Dagster.

### Troubleshooting

- **Connection Issues**: Ensure that your Snowflake and S3 credentials are correct and have the necessary permissions.
- **Data Format Issues**: Verify that the data extracted from Snowflake is correctly formatted before uploading to S3.
- **Resource Configuration**: Double-check the resource configurations in Dagster to ensure they are correctly set up.

### Additional Resources

- [Dagster Snowflake Integration](https://docs.dagster.io/integrations/libraries/snowflake)
- [Dagster AWS S3 Integration](https://docs.dagster.io/integrations/libraries/aws)
- [Pandas Documentation](https://pandas.pydata.org/pandas-docs/stable/)

### Next Steps

- Explore scheduling the data transfer process using Dagster schedules.
- Implement data validation and transformation steps before storing data in S3.
- Integrate with other data processing tools for further analysis and reporting.

This use case demonstrates how to transfer data from Snowflake to Amazon S3 using Dagster. The objective is to automate the extraction of data from Snowflake and store it in S3 for further processing or archival.