Stripe is a software-as-a-service (SaaS) platform that lets businesses accept payments online and in mobile apps.

Snowflake is a cloud-based data warehouse implemented as a managed service. It runs on the Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be fast, flexible, and easy to work with. It provides native support for JSON, Avro, XML, and Parquet data, and can provide access to the same data for multiple workgroups or workloads simultaneously with no contention roadblocks or performance degradation.

You can get data off of Stripe's servers using the Stripe REST API, which exposes information about core resources, payment methods, subscriptions, and more. To get a list of all customers, for instance, you could call GET /v1/customers.

The Stripe API returns JSON-formatted data. Data from a call to retrieve customers might look like this.

{
  "object": "list",
  "url": "/v1/customers",
  "has_more": false,
  "data": [
    {
      "id": "cus_BykTW2x4M6Yrrt",
      "object": "customer",
      "account_balance": 0,
      "created": 1513697132,
      "currency": "usd",
      "default_source": null,
      "delinquent": false,
      "description": null,
      "discount": null,
      "email": null,
      "livemode": false,
      "metadata": {
      },
      "shipping": null,
      "sources": {
        "object": "list",
        "data": [
    
        ],
        "has_more": false,
        "total_count": 0,
        "url": "/v1/customers/cus_BykTW2x4M6Yrrt/sources"
      },
      "subscriptions": {
        "object": "list",
        "data": [
    
        ],
        "has_more": false,
        "total_count": 0,
        "url": "/v1/customers/cus_BykTW2x4M6Yrrt/subscriptions"
      }
    },
    {...},
    {...}
  ]
}

Now you need to parse the JSON in the API response and map each column to a corresponding field in a table in the destination database. You'll have to know the datatypes for each field. The Stitch Stripe Docs can give you a sense of what datatypes will come through the API.

Depending on the structure of your data, you may need to prepare it for loading. Look at the supported data types for Snowflake and make sure that the data you've got will map neatly to them.

Note that you don't need to define a schema in advance when loading JSON data into Snowflake.

Snowflake's Data Loading Overview documentation can help you with loading your data. If you're not loading a lot of data, you might be able to use the data loading wizard in the Snowflake web UI, but chances are that that tool's limitations will make it unsuitable as a reliable ETL solution. Another approach involves two steps for getting data into Snowflake:

• Use the PUT command to stage files.
• Use the COPY INTO table command to load prepared data into an awaiting table.

You can copy the data from your local drive or from Amazon S3. Snowflake lets you make a virtual warehouse that can power the insertion process.

So, now what? You've built a script that pulls data from Stripe and loads it to your destination, but what happens tomorrow when you have hundreds of new transactions?

The key is to build your script in such a way that it can also identify incremental updates to your data. Thankfully, Stripe's API results include fields like "created" that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've taken new transactions into account, you can set up your script as a cron job or continuous loop to keep pulling down new data as it appears.

Snowflake is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.