How to load data from Salesforce Pardot to Redshift

Blendo Team

This post will help you with syncing your Pardot data to Amazon Redshift. By doing this you will be able to perform advanced analytics on a system that is designed for this kind of data payloads, like Amazon Redshift.

Alternatively, you can simplify the process of syncing data from Pardot to Amazon Redshift by using Blendo, where the whole process will be handled by Blendo and you can focus on what matters, the analysis of your marketing data.

Access your data on Salesforce Pardot

The first step in loading your Pardot data to any kind of data warehouse solution is to access your data and start extracting it.

Salesforce was one of the pioneers in the SaaS and API economy and as would someone expect from them, Pardot can be accessed through a web REST API.

Currently, there are two versions of the API that are supported, v3 and v4 but as it should be expected, at some point v3 will be decommissioned as it is there mainly for back compatibility reasons. So, if you plan to integrate with the API, make sure that you choose the right one.

Accessing the data from Pardot through the API is a straightforward process, you perform GET requests, to the relative API endpoints and the API will respond with a result to the query that has been made.

The API is built around 22 different resources that represent anything that someone can do with the marketing automation capabilities of the platform. You will find endpoints to access your Lists or your Visitors.

You will need more time to read this post than integrating Salesforce Pardot to Redshift.

Effortlessly Sync All Your Salesforce Pardot Data to Redshift

The things that you have to keep in mind when dealing with an API like the one Pardot has, are:

  1. Rate limits. Every API has some rate limits that you have to respect. Especially when you are dealing with APIs that are coming from SalesForce, where the API calls are shared among the integrations and the regular product users.
  2. Authentication. You authenticate on Pardot using OAuth, which will add some overhead to the development of an application that will try to pull data out.
  3. Paging and dealing with a big amount of data. Platforms like Pardot tend to generate a lot of data, as they track the interactions of people with your brand. Pulling big volumes of data out of an API might be difficult, especially when you consider and respect any rate limits that the API has.

About Pardot

Pardot or Salesforce Pardot is a B2B marketing automation platform by Salesforce. It offers a very powerful editor to define and execute marketing automation flows for lead generation, nurturing and monitoring of any kind of sales funnels. Of course, as it is a product of the Salesforce family, it also integrates very well with SFDC which is one of the most feature-rich CRM, currently on the market, which makes the choice of using Pardot even more appealing.

Transform and prepare your Pardot data for Amazon Redshift

Amazon Redshift is built around industry-standard SQL with added functionality to manage very large data sets and high-performance analysis. So, in order to load your data into it, you will have to follow its data model which is a typical relational database model. The data you extract from your data source should be mapped into tables and columns. Where you can consider the table as a map to the resource you want to store and columns the attributes of that resource.

Also, each attribute should adhere to the data types that are supported by Redshift.

As your data is probably coming in a representation like JSON that supports a much smaller range of data types you have to be really careful about what data you feed into Redshift and make sure that you have mapped your types into one of the datatypes that are supported by Redshift.

Designing a Schema for Redshift and mapping the data from your data source to it is a process that you should take seriously as it can both affect the performance of your cluster and the questions that you can answer. It’s always a good idea to have in your mind the best practices that Amazon has published regarding the design of a Redshift database. When you have concluded on the design of your database you need to load your data on one of the data sources that are supported as input by Redshift, these are the following:

Load your Pardot data into Amazon Redshift

To upload your data to Amazon S3 you will have to use the AWS REST API, as we see again APIs play an important role in both the extraction but also the loading of data into our data warehouse. The first task that you have to perform is to create a bucket, you do that by executing an HTTP PUT on the Amazon AWS REST API endpoints for S3.

You can do this by using a tool like CURL or Postman. Or use the libraries provided by Amazon for your favorite language. You can find more information by reading the API reference for the Bucket operations on Amazon AWS documentation.

After you have created your bucket you can start sending your data to Amazon S3, using again the same AWS REST API but by using the endpoints for Object operations. As in the Bucket case you can either access the HTTP endpoints directly or use the library of your preference.

Amazon Redshift supports two methods for loading data into it. The first one is by invoking an INSERT command. You can connect to your Amazon Redshift instance with your client, using either a JDBC or ODBC connection and then you perform an INSERT command for your data.

The way you invoke the INSERT command is the same as you would do with any other SQL database, for more information you can check the INSERT examples page on the Amazon Redshift documentation.

Redshift is not designed for INSERT like operations, on the contrary, the most efficient way of loading data into it is by doing bulk uploads using a COPY command.

You can perform a COPY command for data that lives as flat files on S3 or from an Amazon DynamoDB table. When you perform COPY commands, Redshift is able to read multiple files in simultaneously and it automatically distributes the workload to the cluster nodes and performs the load in parallel.

The best way to load data from Salesforce Pardot to Amazon Redshift and possible alternatives

So far we just scraped the surface of what can be done with Amazon Redshift and how to load data into it. The way to proceed relies heavily on the data you want to load, from which service they are coming from and the requirements of your use case. Things can get even more complicated if you want to integrate data coming from different sources.

A possible alternative, instead of writing, hosting and maintaining a flexible data infrastructure, is to use a product like Blendo that can handle this kind of problems automatically for you.

Blendo integrates with multiple sources or services like databases, CRM, email campaigns, analytics and more. Quickly and safely move all your data from Pardot to Redshift and start generating insights from your data.

Help your marketing and executive team take ownership of the insights that live in your Salesforce Pardot marketing platform to transform the performance and ROI of your campaigns.

Blendo is the easiest way to automate powerful data integrations.

Try Blendo free for 14 days. No credit card required.