Braintree Payments to Panoply

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

Braintree, a service of PayPal, provides a payment platform for businesses that want to support online and mobile users.

What is Panoply?

Panoply provides a Smart Cloud Data Warehouse platform that lets users set up a new Amazon Redshift instance in just a few clicks. It uses machine learning algorithms to accomplish 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 use analysis, SQL, and visualization tools on data in Panoply just as you would if you were creating a Redshift data warehouse on your own.

Getting data out of Braintree

Braintree exposes data both through webhooks and an API. Using webhooks lets you push data to a defined HTTP endpoint as events happen. Alternatively, you can pull specific data via the API. A key difference is that the API allows you to retrieve historical data instead of just new data.

The Braintree API lets you get information on things like customer information, payments, and individual transactions. If you wanted information on transactions, for example, you could write code to call the API using SDKs for Java, .NET, Node.js, PHP, Python, and Ruby. Here’s a Python example:

search_results =
    braintree.TransactionSearch.credit_card_card_type == braintree.CreditCard.CardType.Visa

search_results =

If using the API seems like overkill, you can use webhooks to retrieve some categories of data via user-defined HTTP callbacks. To do that, you create a webhook destination URL on your server, create a new webhook using the Braintree Control Panel, then set up your server to parse incoming webhooks.

Sample Braintree data

The object Braintree’s API returns may have dozens of attributes – far too many to list here, even in an example.

Preparing Braintree data

If you don’t already have a data structure in which to store the data you retrieve, you’ll have to create a schema for your data tables. Then, for each value in the response, you’ll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. The source API documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you’ll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into Panoply

Once you've identified the columns you want to insert, you can use the Redshift CREATE TABLE statement to set up a table to receive all of the data.

To populate that table, you might be tempted to use INSERT statements to add data to your Redshift table row by row. Don't do that; Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, a better approach is to load the data into Amazon S3 and use the COPY command to migrate it into Redshift.

Keeping Braintree 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 Braintree.

And remember, as with any code, once you write it, you have to maintain it. If PayPal modifies the Braintree API, or if 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

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, or Snowflake, 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, and To Snowflake.

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 Braintree Payments data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Panoply data warehouse.