Drip to BigQuery

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

What is Drip?

Drip is an online marketing automation platform.

What is Google BigQuery?

Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.

Getting data out of Drip

You can collect data from Drip’s servers using webhooks and user-defined HTTP callbacks. Set up the webhook in your Drip account, and define a URL that your script listens to and from which it can collect the data.

Sample Drip data

Once you've set up webhooks and 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. Here's a sample of what that data might look like.

  "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": "2017-06-21T10:31:58Z"
  "ip_address": "",
  "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

You need to map all the data fields in the JSON data from your webhook into a schema that can be inserted into your database. For each value in the response, you need to identify a predefined datatype (i.e. INTEGER, DATETIME, etc.) and build a table that can receive them.

Loading data into Google BigQuery

Google Cloud Platform offers a helpful guide for loading data into BigQuery. You can use the bq command-line tool to upload the files to your datasets, adding schema and data type information along the way. The bq load command is the workhorse here. You can find its syntax in the bq command-line tool quickstart guide. Iterate through this process as many times as it takes to load all of your tables into BigQuery.

Keeping Drip data up to date

Once you’ve coded up a script or written a program to get the data you want and move it into your data warehouse, you’re going to have to maintain it. If Drip modifies its webhook implementation, or sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

Other data warehouse options

BigQuery 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, PostgreSQL, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To Postgres, To Snowflake, and To Panoply.

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 Google BigQuery data warehouse.