This page provides you with instructions on how to extract data from Pardot 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 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 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 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 Google BigQuery
Google Cloud Platform provides a guide you can follow when you begin loading data into BigQuery. Use the
bq command-line tool, and in particular the
bq load command, to upload data. The syntax is documented in the Quickstart guide for bq. You can supply the table or partition schema, or, for supported data formats, you can use schema auto-detection. Iterate through this process as many times as it takes to load all of your tables and table data into BigQuery.
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
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 Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. 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, To Azure Synapse Analytics, To S3, and To Delta Lake.
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 Google BigQuery automatically. With just a few clicks, Stitch starts extracting your Pardot data, structuring it in a way that's optimized for analysis, and inserting that data into your Google BigQuery data warehouse.