Vero is an event-driven email platform businesses can use to drive customer interaction campaigns.

Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.

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.

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"
        }
    }

Google Cloud Platform offers a helpful guide for loading data into BigQuery. You can use the bq command-line tool to upload the files to your datasets, adding schema and data type information along the way. The bq load command is the workhorse here. You can find its syntax in the bq command-line tool quickstart guide. Iterate through this process as many times as it takes to load all of your tables into BigQuery.

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.

BigQuery 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, PostgreSQL, 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 Postgres, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.