This page provides you with instructions on how to extract data from Braintree Payments and analyze it in Power BI. (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 Power BI?
Power BI is Microsoft’s business intelligence offering. It's a powerful platform that includes capabilities for data modeling, visualization, dashboarding, and collaboration. Many enterprises that use Microsoft's other products can get easy access to Power BI and choose it for its convenience, security, and power.
With high-value use cases across analysts, IT, business users, and developers, Power BI offers a comprehensive set of functionality that has consistently landed Microsoft in Gartner's "Leaders" quadrant for Business Intelligence.
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 Power BI
You can analyze any data in Power BI, as long as that data exists in a data warehouse that's connected to your Power BI account. The most common data warehouses include Amazon Redshift, Google BigQuery, and Snowflake. Microsoft also has its own data warehousing platform called Azure SQL Data Warehouse.
Connecting these data warehouses to Power BI is relatively simple. The Get Data menu in the Power BI interface allows you to import data from a number of sources, including static files and data warehouses. You'll find each of the warehouses mentioned above among the options in the Database list. The Power BI documentation provides more details on each.
Analyzing data in Power BI
In Power BI, each table in the data warehouse you connect is known as a dataset, and the analyses conducted on these datasets are known as reports. To create a report, use Power BI’s report editor, a visual interface for building and editing reports.
The report editor guides you through several selections in the course of building a report: the visualization type, fields being used in the report, filters being applied, any formatting you wish to apply, and additional analytics you may wish to layer onto your report, such as trendlines or averages. You can explore all of the features related to analyzing and tracking data in the Power BI documentation.
Once you've created a report, Power BI lets you share it with report "consumers" in your organization.
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 Power BI 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 Synapse Analytics, 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 Power BI. 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 Power BI.