This page provides you with instructions on how to extract data from Braintree Payments and analyze it in Amazon QuickSight. (If the mechanics of extracting data from Braintree Payments seem too complex or difficult to maintain, 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 QuickSight?
Amazon QuickSight is the AWS business intelligence tool for creating dashboards and visualizations. Users are charged per session only for the time when they access dashboards or reports. QuickSight supports a variety of data sources, such as individual databases (Amazon Aurora, MariaDB, and Microsoft SQL Server), data warehouses (Amazon Redshift and Snowflake), and SaaS sources (Adobe Analytics, GitHub, and Salesforce), along with several common standard file formats.
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.Transaction.search( braintree.TransactionSearch.credit_card_card_type == braintree.CreditCard.CardType.Visa ) search_results = braintree.Transaction.search( braintree.TransactionSearch.credit_card_card_type.in_list( braintree.CreditCard.CardType.Visa, braintree.CreditCard.CardType.MasterCard, braintree.CreditCard.CardType.Discover ) )
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 QuickSight
You must replicate data from your SaaS applications to a data warehouse (such as Redshift) before you can report on it using QuickSight. Once you specify a data source you want to connect to, you must specify a host name and port, database name, and username and password to get access to the data. You then choose the schema you want to work with, and a table within that schema. You can add additional tables by specifying them as new datasets from the main QuickSight page.
Using data in QuickSight
QuickSights provides both a visual report builder and the ability to use SQL to select, join, and sort data. QuickSight lets you combine visualizations into dashboards that you can share with others, and automatically generate and send reports via email.
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.
From Braintree Payments to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Braintree Payments data in Amazon QuickSight is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Braintree Payments to Redshift, Braintree Payments to BigQuery, Braintree Payments to Azure SQL Data Warehouse, Braintree Payments to PostgreSQL, Braintree Payments to Panoply, and Braintree Payments to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Braintree Payments with Amazon QuickSight. With just a few clicks, Stitch starts extracting your Braintree Payments data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Amazon QuickSight.