This page provides you with instructions on how to extract data from Pardot and load it into PostgreSQL. (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 PostgreSQL?
PostgreSQL, or Postgres, is a popular object-relational database management system (ORDBMS) that calls itself "the world's most advanced open source database." PostgreSQL, which is licensed as open source software, offers enterprise-grade features, a strong emphasis on extensibility, and standards compliance.
The database runs on all major operating systems, including Linux, Unix, and Windows. It's ACID-compliant and supports foreign keys, joins, views, triggers, and stored procedures in multiple languages. PostgreSQL is frequently used as a back-end database for web systems and software tools, and is available in cloud-based deployments by most major cloud vendors. The PostgreSQL syntax forms the basis for querying Amazon Redshift, which makes migration between the two systems relatively painless and makes Postgres a good platform for developers who may later work on Redshift's data warehouse platform.
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 Postgres
Once you have identified all of the columns you will want to insert, you can use the
CREATE TABLE statement in Postgres to create a table that can receive all of this data. Then, Postgres offers a number of methods for loading in data, and the best method varies depending on the quantity of data you have and the regularity with which you plan to load it.
For simple, day-to-day data insertion, running
INSERT queries against the database directly are the standard SQL method for getting data added. Documentation on INSERT queries and their bretheren can be found in the Postgres documentation here.
For bulk insertions of data, which you will likely want to conduct if you have a high volume of data to load, other tools exist as well. This is where the
COPY command becomes quite useful, as it allows you to load large sets of data into Postgres without needing to run a series of INSERT statements. Documentation can be found here.
The Postgres documentation also provides a helpful overall guide for conducting fast data inserts, populating your database, and avoiding common pitfalls in the process. You can find it here.
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
PostgreSQL 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, Snowflake, or Microsoft Azure SQL Data Warehouse, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Snowflake, To Panoply, To Azure SQL Data Warehouse, and To S3.
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 Pardot to PostgreSQL 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 your PostgreSQL data warehouse.