This page provides you with instructions on how to extract data from Vero 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 Vero?
Vero is an event-driven email platform businesses can use to drive customer interaction campaigns.
What is PostgreSQL?
PostgreSQL, or Postgres, is a popular object-relational database management system (ORDBMS) that calls itself "the world's most advanced open source database." It offers enterprise-grade features, maintains a strong emphasis on extensibility, and is licensed as open source software.
PostgreSQL runs on all major operating systems, including Linux, Unix, and Windows. It's ACID-compliant and supports foreign keys, joins, views, triggers, and stored procedures in multiple languages. PostgreSQL is frequently used as a back-end database for web systems, 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 platform for developers who may eventually work on Amazon's data warehouse platform.
Getting data out of Vero
You can collect that data from Vero's servers using webhooks and user-defined HTTP callbacks. Set up the webhook in your Vero account and define a URL that your script listens to and from which it can collect data.
Sample Vero data
Once you've set up HTTP endpoints, Vero will begin sending data via the POST request method. You can access useful objects such as sent, delivered, opened, clicked, bounced, and unsubscribed. Data will be enclosed in the body of the request in JSON format. Here's a sample of what an inbound webhook with data from the Vero endpoint looks like.
{ "sent_at":1435016238, "type":"sent", "user": { "id":123, "email":"steve@getvero.com" }, "campaign": { "id":987, "type":"transactional", "name":"Order confirmation", "subject":"Your order is being processed!", "trigger-event":"purchased item", "permalink":"http://app.getvero.com/view/1/341d64944577ac1f70f560e37db54a25", "variation":"Variation A" } }
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 Vero 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 Vero.
And remember, as with any code, once you write it, you have to maintain it. If Vero 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 Vero to PostgreSQL automatically. With just a few clicks, Stitch starts extracting your Vero data, structuring it in a way that's optimized for analysis, and inserting that data into your PostgreSQL data warehouse.