This page provides you with instructions on how to extract data from Salesforce Marketing Cloud Email Studio and load it into PostgreSQL. (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 Salesforce Marketing Cloud?
Salesforce Marketing Cloud is a marketing automation platform for B2B and B2C marketing. Salesforce Marketing Cloud Email Studio (known as ExactTarget before the company that created it was purchased by Salesforce in 2013) lets businesses create scalable data-based email marketing campaigns. The data it tracks (e.g. open and bounce rates) can be accessed by other parts of the Marketing Cloud platform, which also comprises social media marketing, digital advertising, and mobile messaging components.
What is PostgreSQL?
PostgreSQL, also known as Postgres, calls itself "the world's most advanced open source database." The popular object-relational database management system (ORDBMS) offers enterprise-grade features with a strong emphasis on extensibility and standards compliance.
PostgreSQL runs on all major operating systems, including Linux, Unix, and Windows. It's open source, fully ACID-compliant, and has full support for foreign keys, joins, views, triggers, and stored procedures in multiple languages. PostgreSQL is often the best back-end database for web systems and software tools. It's available in cloud-based deployments by most major cloud vendors. And since its syntax forms the basis for querying Amazon Redshift, which makes migration between the two systems relatively painless, Postgres a good stepping-stone for developers who may later use Redshift's data warehouse platform.
Getting data out of Salesforce Marketing Cloud
Marketing Cloud offers two APIs:
- A REST API that exposes access to a range of Marketing Cloud capabilities
- A SOAP API that provides access to most email functionality, including tracking, subscribers and lists, automations, and content
The SOAP API uses SOAP envelopes to pass SOAP data. A call to retrieve all messages sent since the last batch might look like this.
<Envelope xmlns="http://schemas.xmlsoap.org/soap/envelope/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <Header> <Security xmlns="https://www.marketingcloud.com/"> <fueloauth>YOUR_ACCESS_TOKEN</fueloauth> </Security> </Header> <Body> <RetrieveRequestMsg xmlns="http://exacttarget.com/wsdl/partnerAPI"> <RetrieveRequest> <ObjectType>SentEvent</ObjectType> <Properties>SubscriberKey</Properties> <Properties>EventDate</Properties> <QueryAllAccounts>false</QueryAllAccounts> <Filter xsi:type="SimpleFilterPart"> <Property>SendID</Property> <SimpleOperator>equals</SimpleOperator> <Value>12345</Value> </Filter> <RetrieveAllSinceLastBatch>true</RetrieveAllSinceLastBatch> </RetrieveRequest> </RetrieveRequestMsg> </Body> </Envelope>
Sample Salesforce Marketing Cloud Email Studio data
The Email Studio API returns information in a SOAP envelope. You have to parse all the attributes before loading the data into your data warehouse. Here's an example of what some of the data for that call to retrieve all messages might look like.
<soap:Envelope xmlns:soap="http://schemas.xmlsoap.org/soap/envelope/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:wsa="http://schemas.xmlsoap.org/ws/2004/08/addressing" xmlns:wsse="http://docs.oasis-open.org/wss/2004/01/oasis-200401-wss-wssecurity-secext-1.0.xsd" xmlns:wsu="http://docs.oasis-open.org/wss/2004/01/oasis-200401-wss-wssecurity-utility-1.0.xsd"> <soap:Header> <wsa:Action>RetrieveResponse</wsa:Action> <wsa:MessageID>urn:uuid:cdb7b621-8341-4a35-840d-c4ec56fb8a6d</wsa:MessageID> <wsa:RelatesTo>urn:uuid:e6a83541-3ae7-412d-9412-d40ee321c4aa</wsa:RelatesTo> <wsa:To>http://schemas.xmlsoap.org/ws/2004/08/addressing/role/anonymous</wsa:To> </soap:Header> <soap:Body> <RetrieveResponseMsg xmlns="http://exacttarget.com/wsdl/partnerAPI"> <OverallStatus>OK</OverallStatus> <RequestID>cefcfd29-d44f-4bbd-9188-e2bd51e2de5f</RequestID> <Results xsi:type="SentEvent"> <PartnerKey xsi:nil="true"/> <ObjectID xsi:nil="true"/> <SubscriberKey>email@example.com</SubscriberKey> <EventDate>2017-03-26T10:02:01.987</EventDate> </Results> <Results xsi:type="SentEvent"> <PartnerKey xsi:nil="true"/> <ObjectID xsi:nil="true"/> <SubscriberKey>firstname.lastname@example.org</SubscriberKey> <EventDate>2014-03-26T10:02:01.987</EventDate> </Results> <Results xsi:type="SentEvent"> <PartnerKey xsi:nil="true"/> <ObjectID xsi:nil="true"/> <SubscriberKey>email@example.com</SubscriberKey> <EventDate>2017-03-26T10:02:01.987</EventDate> </Results> </RetrieveResponseMsg> </soap:Body> </soap:Envelope>
Preparing Salesforce Marketing Studio 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. Salesforce'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 Postgres
Once you have identified all of the columns you will want to insert, you can use the
CREATE TABLE statement in Postgres to create a table that can receive all of this data. Then, Postgres offers a number of methods for loading in data, and the best method varies depending on the quantity of data you have and the regularity with which you plan to load it.
For simple, day-to-day data insertion, running
INSERT queries against the database directly are the standard SQL method for getting data added. Documentation on INSERT queries and their bretheren can be found in the Postgres documentation here.
For bulk insertions of data, which you will likely want to conduct if you have a high volume of data to load, other tools exist as well. This is where the
COPY command becomes quite useful, as it allows you to load large sets of data into Postgres without needing to run a series of INSERT statements. Documentation can be found here.
The Postgres documentation also provides a helpful overall guide for conducting fast data inserts, populating your database, and avoiding common pitfalls in the process. You can find it here.
Keeping Salesforce Marketing Cloud 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 Email Studio.
And remember, as with any code, once you write it, you have to maintain it. If Salesforce 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
PostgreSQL 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, 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 BigQuery, 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 Salesforce Marketing Cloud Email Studio to PostgreSQL automatically. With just a few clicks, Stitch starts extracting your Salesforce Marketing Cloud Email Studio data, structuring it in a way that's optimized for analysis, and inserting that data into your PostgreSQL data warehouse.