FullStory to BigQuery

This page provides you with instructions on how to extract data from FullStory 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 FullStory?

The FullStory digital intelligence platform lets you replay customers' website journeys to solve problems, find answers, and optimize customers' experience. It features funnel analytics, click maps, and robust search and segmentation.

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 FullStory

You can use the FullStory API to get a list of sessions for a particular user. For example, to get information based on a user's email address, you could GET https://www.fullstory.com/api/v1/sessions?email=john@example.com.

Sample FullStory data

Here's an example of the kind of response you might see with a query like the one above.

[{
 "UserId": 1234567890,
 "SessionId": 1234567890,
 "CreatedTime": 1411492739,
 "FsUrl": "https://www.fullstory.com/ui/ORG_ID/discover/session/1234567890:1234567890"
}]

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 awaiting datasets, adding the correct schema and data type information along the way. The bq load command is your friend here. You can find the 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 FullStory data up to date

Now what? You've built a script that pulls data from FullStory and loads it into your data warehouse, but what happens tomorrow when you have new transactions?

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

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, Snowflake, or Microsoft Azure SQL Data Warehouse, 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, To Panoply, and To Azure SQL Data Warehouse.

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 move data from FullStory to Google BigQuery automatically. With just a few clicks, Stitch starts extracting your FullStory data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Google BigQuery data warehouse.