Pardot to Panoply

This page provides you with instructions on how to extract data from Pardot and load it into Panoply. (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 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 Panoply?

Panoply is a fully managed data warehouse service that can spin up an Amazon Redshift instance in just a few clicks. It uses machine learning and natural language processing (NLP) to learn, model, and automate standard data management activities from source to analysis. It can import data with no schema, no modeling, and no configuration. With Panoply, you can use your favorite analysis, SQL, and visualization tools just as you would if you were creating a Redshift data warehouse on your own.

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 Panoply

Once you have identified all of the columns you want to insert, you can use the CREATE TABLE statement in Panoply's Redshift data warehouse to create a table to receive all of the data.

With a table built, it may seem like the easiest way to migrate your data (especially if there isn't much of it) is to build INSERT statements to add data to your Redshift table row by row. If you have any experience with SQL, this will be your gut reaction. But beware! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you would be better off loading the data into Amazon S3 and then using the COPY command to load it into Redshift.

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.

Other data warehouse options

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

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 Pardot data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Panoply data warehouse.