This page provides you with instructions on how to extract data from Pardot and analyze it in Grafana. (If the mechanics of extracting data from Pardot 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 Pardot?
Pardot, a marketing automation platform owned by Salesforce, helps businesses attract, convert, and retain customers. It uses automation tools to powers engagement campaigns designed to help companies generate leads and close sales.
What is Grafana?
Grafana is an open source platform for time series analytics. It can run on-premises on all major operating systems or be hosted by Grafana Labs via GrafanaCloud. Grafana allows users to create, explore, and share dashboards to query, visualize, and alert on data.
Getting data out of Pardot
The Pardot REST API gives developers access to prospects, visitors, activities, opportunities, and other data in Pardot. By default, Pardot Pro customers are allocated 25,000 API requests per day, and Pardot Ultimate customers can make up to 100,000.
A call to the Pardot API for prospect information might look like
GET /api/prospect/version/4/do/query, with required security and authentication parameters tacked on at the end, along with optional selection parameters that let you tailor what data is returned.
Sample Pardot data
Responses to Pardot API calls come in the form of XML files. A barebones example of the kind of data you might see looks like this:
<rsp stat="ok" version="1.0"> <result> <total_results>...</total_results> <prospect>...</prospect> ... </result> </rsp>
Preparing Pardot data
If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Pardot's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.
Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.
Loading data into Grafana
Analyzing data in Grafana requires putting it into a format that Grafana can read. Grafana natively supports nine data sources, and offers plugins that provide access to more than 50 more. Generally, it's a good idea to move all your data into a data warehouse for analysis. MySQL, Microsoft SQL Server, and PostgreSQL are among the supported data sources, and because Amazon Redshift is built on PostgreSQL and Panoply is built on Redshift, those popular data warehouses are also supported. However, Snowflake and Google BigQuery are not currently supported.
Analyzing data in Grafana
Grafana provides a getting started guide that walks new users through the process of creating panels and dashboards. Panel data is powered by queries you build in Grafana's Query Editor. You can create graphs with as many metrics and series as you want. You can use variable strings within panel configuration to create template dashboards. Time ranges generally apply to an entire dashboard, but you can override them for individual panels.
Keeping Pardot data up to date
At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.
Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Pardot.
And remember, as with any code, once you write it, you have to maintain it. If Pardot modifies its API, or the API 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.
From Pardot to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Pardot data in Grafana 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 Pardot to Redshift, Pardot to BigQuery, Pardot to Azure SQL Data Warehouse, Pardot to PostgreSQL, Pardot to Panoply, and Pardot to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data from Pardot to Grafana automatically. With just a few clicks, Stitch starts extracting your Pardot data via the API, 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 Grafana.