How to load data from Pardot to Redshift
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.
Accessing the data from Pardot through their API is a straightforward process, you perform GET requests, to the relative API endpoints and it will respond with a result to the query that has been made.
The Pardot 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.
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.
Prepare your Pardot data for Amazon Redshift Replication
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 move data into it, you will have to follow its data model which is a typical relational database model. The data you extract from a 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 the 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 data types that are supported by Redshift.
Designing a Schema for Redshift and mapping the data from a 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 data on one of the data sources that are supported as input by Redshift, these are the following:
Export data from Pardot to Redshift
To upload 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 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.
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 simultaneously and it automatically distributes the workload to the cluster nodes and performs the load in parallel.
If you are looking into other data warehouses you may check our how to’s on Pardot to BigQuery, Pardot to MS SQL Server, Pardot to Snowflake, Pardot to PostgreSQL.
The best way to load data from Pardot to Redshift
So far we just scraped the surface of what can be done with Amazon Redshift and how to ingest 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. If these instructions look cumbersome, instead of writing, hosting, and maintaining a data infrastructure, is to use a product like RudderStack that can handle this kind of problem automatically for you.
Easily use the Salesforce Pardot connector from RudderStack, along with multiple sources or services like databases, CRM, email campaigns, analytics, and more. Quickly and safely ingest Pardot data into Amazon Redshift and start generating insights from your data.