
## Ingesting Data from S3 to Snowflake with Dagster and Sling

This guide provides a step-by-step approach to ingest data from Amazon S3 into Snowflake using Dagster and the Sling integration from dagster-embedded-elt. The main objective is to automate the data ingestion process, making it efficient and reliable for data management and analysis.

---

## What You'll Learn

By following this guide, you will learn how to:

- Set up connections to S3 and Snowflake using Sling.
- Define and configure assets in Dagster to automate the data ingestion process.
- Execute the data ingestion pipeline.

---

## Prerequisites

Before you begin, ensure you have the following:

- A Snowflake account with the necessary permissions.
- An Amazon S3 bucket with data to ingest.
- AWS credentials with access to the S3 bucket.
- Python installed on your system.
- Dagster and dagster-embedded-elt installed in your Python environment.

---

## Steps to Implement With Dagster

By following these steps, you will have an automated pipeline that ingests data from Amazon S3 into Snowflake. The data will be available in Snowflake for further processing and analysis.

### Step 1: Install Required Packages

Ensure you have the necessary Python packages installed. Install Dagster and the Dagster UI (`dagster-webserver`) and dagster-embedded-elt using pip. Refer to the [Installation guide](https://docs.dagster.io/getting-started/install) for more info.

```bash
pip install dagster dagster-embedded-elt dagster-webserver
```

### Step 2: Define Sling Connections

Define the connections to S3 and Snowflake using SlingConnectionResource. Use environment variables to securely manage sensitive information.

```python
from dagster import EnvVar
from dagster_embedded_elt.sling import SlingConnectionResource, SlingResource

s3_connection = SlingConnectionResource(
    name="MY_S3",
    type="s3",
    bucket="your-s3-bucket",
    access_key_id=EnvVar("AWS_ACCESS_KEY_ID"),
    secret_access_key=EnvVar("AWS_SECRET_ACCESS_KEY"),
)

snowflake_connection = SlingConnectionResource(
    name="MY_SNOWFLAKE",
    type="snowflake",
    host="your-snowflake-host",
    user="your-snowflake-user",
    database="your-snowflake-database",
    password=EnvVar("SNOWFLAKE_PASSWORD"),
    role="your-snowflake-role",
)

sling_resource = SlingResource(connections=[s3_connection, snowflake_connection])
```

### Step 3: Define the Data Ingestion Asset

Use the `@sling_assets` decorator to define an asset that runs the Sling replication job. Configure the replication settings to specify the source and target.

```python
from dagster import Definitions
from dagster_embedded_elt.sling import sling_assets

replication_config = {
    "SOURCE": "MY_S3",
    "TARGET": "MY_SNOWFLAKE",
    "defaults": {"mode": "full-refresh", "object": "{stream_schema}_{stream_table}"},
    "streams": {
        "s3://your-s3-bucket/your-file.csv": {
            "object": "your_snowflake_schema.your_table",
            "primary_key": "id",
        },
    },
}

@sling_assets(replication_config=replication_config)
def ingest_s3_to_snowflake(context, sling: SlingResource):
    yield from sling.replicate(context=context)

defs = Definitions(assets=[ingest_s3_to_snowflake], resources={"sling": sling_resource})
```

---

## Expected Outcomes

After implementing this use case, you should have an automated pipeline that ingests data from Amazon S3 into Snowflake. The data will be available in Snowflake for further processing and analysis.

---

## Troubleshooting

- **Connection Issues**: Ensure that your AWS and Snowflake credentials are correctly set in the environment variables.
- **Data Format Issues**: Verify that the data format in S3 matches the expected format in Snowflake.
- **Permissions**: Ensure that the Snowflake user has the necessary permissions to write to the target schema and table.

---

## Additional Resources

- [Dagster Documentation](https://docs.dagster.io/)
- [Embedded ELT Documentation](https://docs.dagster.io/integrations/embedded-elt)
- [Sling Documentation](https://docs.slingdata.io/)
- [Snowflake Documentation](https://docs.snowflake.com/)

---

## Next Steps

- Explore more advanced configurations and transformations using Sling.
- Integrate additional data sources and targets to expand your data pipeline.
- Implement monitoring and alerting for your data ingestion pipeline.

By following these steps, you can efficiently automate the process of ingesting data from S3 to Snowflake using Dagster and Sling.


This use case demonstrates how to ingest data from Amazon S3 into Snowflake using Dagster and the Sling integration from dagster-embedded-elt. The objective is to automate the data ingestion process for efficient data management and analysis.