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.

Snowflake is a cloud-native data warehouse that runs on an Amazon Web Services platform. Snowflake is designed to be fast, flexible, and easy to work with. It provides native support for JSON, Avro, XML, and Parquet. Users pay for only the storage and compute resources they use, and can scale storage and compute resources separately.

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.

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>

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.

Depending on how your data is structured, you may need to prepare it for loading. Read about the supported data types for Snowflake and make sure that your data maps well to them.

Note that you don't need to define a schema in advance when loading JSON data into Snowflake.

Snowflake's Data Loading Overview documentation can help you with loading your data. If you're not loading a lot of data, you might be able to use the data loading wizard in the Snowflake web UI, but chances are that that tool's limitations will make it unsuitable as a reliable ETL solution. Another approach involves two steps for getting data into Snowflake:

• Use the PUT command to stage files.
• Use the COPY INTO table command to load prepared data into an awaiting table.

You can copy the data from your local drive or from Amazon S3. Snowflake lets you make a virtual warehouse that can power the insertion process.

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.

Snowflake 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 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 BigQuery, To Postgres, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.