FullStory to Metabase

This page provides you with instructions on how to extract data from FullStory and analyze it in Metabase. (If the mechanics of extracting data from FullStory seem too complex or difficult to maintain, 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 Metabase?

Metabase provides a visual query builder that lets users generate simple charts and dashboards, and supports SQL for gathering data for more complex business intelligence visualizations. It runs as a JAR file, and its developers make it available in a Docker container and on Heroku and AWS. Metabase is free of cost and open source, licensed under the AGPL.

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 Metabase

Metabase works with data in databases; you can't use it as a front end for a SaaS application without replicating the data to a data warehouse first. Out of the box Metabase supports 15 database sources, and you can download 10 additional third-party database drivers, or write your own. Once you specify the source, you must specify a host name and port, database name, and username and password to get access to the data.

Using data in Metabase

Metabase supports three kinds of queries: simple, custom, and SQL. Users create simple queries entirely through a visual drag-and-drop interface. Custom queries use a notebook-style editor that lets users select, filter, summarize, and otherwise customize the presentation of the data. The SQL editor lets users type or paste in SQL queries.

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.

From FullStory to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing FullStory data in Metabase is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites FullStory to Redshift, FullStory to BigQuery, FullStory to Azure Synapse Analytics, FullStory to PostgreSQL, FullStory to Panoply, and FullStory to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate FullStory with Metabase. With just a few clicks, Stitch starts extracting your FullStory data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Metabase.