Klaviyo to Postgres

This page provides you with instructions on how to extract data from Klaviyo 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 Klaviyo?

Klaviyo is an ecommerce marketing automation platform that lets organizations run data-driven email and Facebook campaigns.

What is PostgreSQL?

PostgreSQL, a.k.a. Postgres, proclaims itself "the world's most advanced open source database." The popular open source 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 fully ACID-compliant and supports a roster of features: foreign keys, joins, views, triggers, and stored procedures in multiple languages. PostgreSQL serves as the back end for many web systems and software tools, and is available in cloud-based deployments from most major cloud vendors. PostgreSQL's syntax forms the basis for querying Amazon Redshift, which makes migration between the two systems relatively painless, and makes Postgres a good stepping-stone for developers who may later use Redshift's data warehouse platform.

Getting data out of Klaviyo

Klaviyo exposes data through several REST APIs, which developers can use to extract information on metrics, profiles, lists, campaigns, and templates. Each of these APIs has two to seven optional parameters you can use to refine the information returned. As an example, a simple call to retrieve data via the Klaviyo Metrics API would look like:

GET https://a.klaviyo.com/api/v1/metrics

The GET call returns a JSON object with all the fields of the specified dataset as a reply. All fields may not be present for any given record. The JSON might look like:

{
  "end": 1,
  "object": "$list",
  "page_size": 50,
  "start": 0,
  "total": 2,
  "data": [
    {
      "updated": "2017-11-03 17:28:09",
      "name": "Active on Site",
      "created": "2017-11-03 17:28:09",
      "object": "metric",
      "id": "3vtCwa",
      "integration": {
        "category": "API",
        "object": "integration",
        "id": "4qYGmQ",
        "name": "API"
      }
    },
    {
      "updated": "2017-11-03 20:54:40",
      "name": "Added integration",
      "created": "2017-11-03 20:54:40",
      "object": "metric",
      "id": "8qYK7L",
      "integration": {
        "category": "API",
        "object": "integration",
        "id": "4qYGmQ",
        "name": "API"
      }
    ]
  }

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 Klaviyo 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 Klaviyo.

And remember, as with any code, once you write it, you have to maintain it. If Klaviyo 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 Klaviyo data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your PostgreSQL data warehouse.