Drip to Snowflake

This page provides you with instructions on how to extract data from Drip and load it into Snowflake. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

About Snowflake

Snowflake is a data warehouse solution that is entirely cloud based. It's a managed service. If you don't want to deal with hardware, software, or upkeep for a data warehouse you're going to love Snowflake. It runs on the wicked fast Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be flexible and easy to work with where other relational databases are not. One example of this is the query execution. Snowflake creates virtual warehouses where query processing takes place. These virtual warehouses run on separate compute clusters, so querying one of these virtual warehouses doesn't slow down the others. If you have ever had to wait for a query to complete, you know the value of speed and efficiency for query processing.

Getting data out of Drip

The first step for getting your Drip data into your data warehouse is collecting that data from Drip’s servers. You can do this using Webhooks. Documentation for Drip Webhooks integration can be found here.

Data from Drip can be retrieved via user-defined HTTP callbacks. The first thing you need to do is set up the webhook in your Drip account. After that you need somewhere to send the data. This could be a special URL that your script listens to.

Sample Drip data

Once you’ve set up HTTP endpoints, Drip will begin sending data via the POST request method. Data will be enclosed in the body of the request in JSON format. Below is a sample of what that data looks like when Drip sends data from the subscriber endpoint.

{
  "id": "z1togz2hcjrkpp5treip",
  "status": "active",
  "email": "john@acme.com",
  "custom_fields": {
    "name": "John Doe"
  },
  "tags": ["Customer", "SEO"],
  "time_zone": "America/Los_Angeles",
  "utc_offset": -440,
  "created_at": "2013-06-21T10:31:58Z"
  "ip_address": "123.123.123.123",
  "user_agent": "Mozilla/5.0",
  "lifetime_value": 2000,
  "original_referrer": "https://google.com/search",
  "landing_url": "https://www.drip.co/landing",
  "prospect": true,
  "base_lead_score": 30,
  "lead_score": 65,
  "user_id": "123"
}

Preparing Drip data

With the JSON in hand, you now need to map all those data fields into a schema that can be inserted into your database. This means that for each value in the response, you need to identify a predefined data type (i.e. INTEGER, DATETIME, etc.) and build a table that can receive them.

The Drip documentation can give you a good sense of what fields will be provided by each endpoint, along with their corresponding data types.

Preparing data for Snowflake

Depending on the structure that you data is in, you may need to prepare it for loading. Take a look at the supported data types for Snowflake and make sure that the data you've got will map neatly to them. If you have a lot of data, you should compress it. Gzip, bzip2, Brotli, Zstandard v0.8 and deflate/raw deflate compression types are all supported.

One important thing to note here is that you don't need to define a schema in advance when loading JSON data into Snowflake. Onward to loading!

Loading data into Snowflake

There is a good reference for this step in the Data Loading Overview section of the Snowflake documentation. If there isn’t much data that you’re trying to load, then you might be able to use the data loading wizard in the Snowflake web UI. Chances are, the limitations on that tool will make it a non-starter as a reliable ETL solution. There two main steps to getting data into Snowflake:

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

For the COPY step, you’ll have the option of copying from your local drive, or from Amazon S3. One of Snowflakes’ slick features lets you to make a virtual warehouse that will power the insertion process.

Keeping Drip data up to date

So what’s next? You’ve built a script that collects data from Drip and moves into your data warehouse. What happens when Drip sends a data type that your script doesn’t recognize? It’s also important to consider the situation where an entry in Redshift needs to be updated to a new value. You'll need to build in that functionality. After that, set your script up as a cron job or continuous loop to keep pulling new data as it is posted by Drip.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Drip data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Snowflake data warehouse.