This page provides you with instructions on how to extract data from Listrak 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 Listrak?
Listrak is a marketing automation platform for online and omnichannel retailers. It combines data from desktop and mobile platforms with online and offline data about purchases, and uses AI and predictive analytics to give businesses insights into their customers' behavior.
What is PostgreSQL?
PostgreSQL, known by most simply as Postgres, is a hugely popular object-relational database management system (ORDBMS). It labels itself as "the world's most advanced open source database," and for good reason. The platform, despite being available for free via an open source license, offers enterprise-grade features including a strong emphasis on extensibility and standards compliance.
It runs on all major operating systems, including Linux, Unix, and Windows. It is fully ACID-compliant, has full support for foreign keys, joins, views, triggers, and stored procedures (in multiple languages). Postgres is often the best tool for the job as a back-end database for web systems and software tools, and cloud-based deployments are offered by most major cloud vendors. Its syntax also forms the basis for querying Amazon Redshift, which makes migration between the two systems relatively painless and makes Postgres a good "first step" for developers who may later expand into Redshift's data warehouse platform.
Getting data out of Listrak
Listrak offers a SOAP API that lets developers build, manage, automate, report, and streamline email marketing processes.
A SOAP envelope to request a collection of saved messages might look like this.
POST /v31/IntegrationService.asmx HTTP/1.1 Host: webservices.listrak.com Content-Type: application/soap+xml; charset=utf-8 Content-Length: length <?xml version="1.0" encoding="utf-8"?> <soap12:Envelope xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:soap12="http://www.w3.org/2003/05/soap-envelope"> <soap12:Header> <WSUser xmlns="http://webservices.listrak.com/v31/"> <UserName>string</UserName> <Password>string</Password> </WSUser> </soap12:Header> <soap12:Body> <GetSavedMessageCollection xmlns="http://webservices.listrak.com/v31/"> <ListID>int</ListID> </GetSavedMessageCollection> </soap12:Body> </soap12:Envelope>
Sample Listrak data
A SOAP envelope that contains a response to the request for a collection of saved messages might look like this. In this listing, we're showing datatype strings in place of actual values.
<?xml version="1.0" encoding="utf-8"?> <soap12:Envelope xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:soap12="http://www.w3.org/2003/05/soap-envelope"> <soap12:Body> <GetSavedMessageCollectionResponse xmlns="http://webservices.listrak.com/v31/"> <GetSavedMessageCollectionResult> <WSSavedMessage> <SavedMsgID>int</SavedMsgID> <SavedName>string</SavedName> <CreateDate>dateTime</CreateDate> <ModifiedDate>dateTime</ModifiedDate> </WSSavedMessage> <WSSavedMessage> <SavedMsgID>int</SavedMsgID> <SavedName>string</SavedName> <CreateDate>dateTime</CreateDate> <ModifiedDate>dateTime</ModifiedDate> </WSSavedMessage> </GetSavedMessageCollectionResult> </GetSavedMessageCollectionResponse> </soap12:Body> </soap12:Envelope>
Preparing Listrak 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. Listrak'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 Listrak 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 Listrak.
And remember, as with any code, once you write it, you have to maintain it. If Listrak 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, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. 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, and To Panoply.
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 solve this problem automatically. With just a few clicks, Stitch starts extracting your Listrak data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your PostgreSQL data warehouse.