Autopilot to Redshift

This page provides you with instructions on how to extract data from Autopilot and load it into Redshift. (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 Autopilot?

Autopilot is a visual tool that allows marketers to track their prospects' customer journeys. Some of the information stored in Autopilot is valuable input for business analytics.

Getting data out of Autopilot

Autopilot exposes data through a REST API, which developers can use to extract information. For example, to retrieve a batch of 100 contacts, you could call GET /v1/contacts.

The call returns a JSON object with two or three properties as a reply:

  • total_contacts: the total number of contacts
  • contacts: the current batch of 100 contacts
  • bookmark: if there are more contacts on the list, the bookmark allows you to access the next group of contacts via another GET call.

Each Autopilot contact may have any or all of 26 standard fields, along with any custom fields you may have defined.

Loading data into Redshift

Once you have identified all of the columns you will want to insert, you can use the CREATE TABLE statement in Redshift to create a table that can receive all of this data.

With a table built, it may seem like the easiest way to migrate your data (especially if there isn't much of it) is to build INSERT statements to add data to your Redshift table row by row. If you have any experience with SQL, this will be your gut reaction. But beware! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you would be better off loading the data into Amazon S3 and then using the COPY command to load it into Redshift.

Keeping Autopilot 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 Autopilot.

And remember, as with any code, once you write it, you have to maintain it. If Autopilot modifies its API, or 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

Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, 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 BigQuery, To Postgres, 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 Autopilot data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Redshift data warehouse.