How to load data from Salesforce Pardot to PostgreSQL
This post will help you with export your Pardot to PostgreSQL. If you think this needs time, you may use the Salesforce Pardot Connector for PostgreSQL from RudderStack. With a few clicks, you will start collecting analytics-ready data, consistently into your PostgreSQL instance. No need for scripts or engineering effort and resources, just replicate your data and 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 REST.
Accessing the data from Pardot through the API is a straightforward process. You perform GET requests, to the relative endpoints to get 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.
The things that you have to keep in mind when dealing with an API like the one Pardot has, are:
- Rate limits. Every API has some rate limits that you have to respect. Especially when you are dealing with those coming from Salesforce, where the calls are shared among the integrations and the regular product users.
- 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.
- 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.
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 PostgreSQL Replication
After you have accessed your data on Pardot, you will have to transform it based on two main factors,
- The limitations of the database that the data will be loaded onto
- The type of analysis that you plan to perform
Each system has specific limitations on the data types and data structures that it supports. If for example, you want to push data into Google BigQuery, then you can send nested data like JSON directly. But when you are dealing with tabular data stores, like PostgreSQL, this is not an option. Instead, you will have to flatten out your data before loading it into the database.
Also, you have to choose the right data types. Again, depending on the system that you will send the data to and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit the expressivity of your queries and limit your analysts on what they can do directly out of the database.
Each table is a collection of columns with a predefined data type as an integer or VARCHAR. PostgreSQL, like any other SQL database, supports a wide range of different data types.
A typical strategy for loading data from Pardot to a Postgres database is to create a schema where you will map each endpoint to a table. Each key inside the endpoint response should be mapped to a column of that table and you should ensure the right conversion to a Postgres compatible data type.
For example, if an endpoint from Pardot returns a value as String, you should convert it into a VARCHAR with a predefined max size or TEXT data type. Tables can then be created on your database using the CREATE SQL statement.
Export data from Pardot to PostgreSQL
Once you have defined your schema and you have created your tables with the proper data types, you can start loading data into your database. The preferred way of adding larger datasets into a PostgreSQL database is by using the COPY command. COPY is copying data from a file on a file system that is accessible by the Postgres instance, in this way much larger datasets can be inserted into the database in less time. COPY requires physical access to a file system in order to load data. Nowadays, with cloud-based, fully managed databases, getting direct access to a file system is not always possible. If this is the case and you cannot use a COPY statement, then another option is to use PREPARE together with INSERT, to end up with optimized and more performant INSERT queries.
Updating your Pardot data on PostgreSQL
As you will be generating more data on Pardot, you will need to update your older data on PostgreSQL. This includes new records together with updates to older records that for any reason have been updated on Pardot.
You will need to periodically check Pardot for new data and repeat the process that has been described previously while updating your currently available data if needed. Updating an already existing row on a PostgreSQL table is achieved by creating UPDATE statements.
Another issue that you need to take care of is the identification and removal of any duplicate records on your database. Either because Pardot does not have a mechanism to identify new and updated records or because of errors on your data pipelines, duplicate records might be introduced to your database.
In general, ensuring the quality of the data that is inserted in your database is a big and difficult issue and PostgreSQL features like TRANSACTIONS can help tremendously, although they do not solve the problem in the general case.
The best way to load data from Salesforce Pardot to PostgreSQL
So far we just scraped the surface of what you can do with PostgreSQL and how to ingest data into it. Things can get even more complicated if you want to integrate data coming from different sources.
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