This page provides you with instructions on how to extract data from Autopilot and load it into Panoply. (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.
What is Panoply?
Panoply provides a managed data warehouse platform that lets users quickly set up a new Amazon Redshift instance. It uses machine learning algorithms to handle complex tasks like schema building, data mining, modeling, scaling, performance tuning, security, and backup. Panoply can import data with no schema, no modeling, and no configuration, and you can work with the analysis, SQL, and visualization tools you already know on data in Panoply just as you would if you were creating a Redshift data warehouse manually.
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
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 Panoply
Once you've identified all the columns you want to insert, you can use the CREATE TABLE statement in Reshift to set up a table to receive your data.
With the table built, you might think that the easiest way to migrate your data (especially if there isn't much of it) would be to build INSERT statements to add data to your Redshift table row by row. Think again! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, we suggest moving 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
Panoply 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, PostgreSQL, Snowflake, or Microsoft Azure SQL Data Warehouse, which are RDBMSes that use similar SQL syntax. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, and To Azure SQL Data Warehouse.
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 Panoply data warehouse.