This page provides you with instructions on how to extract data from Recurly and load it into Google BigQuery. (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 Recurly?
Recurly, a software-as-a-service (SaaS) billing management platform, enables businesses to process payments across several payment channels.
What is Google BigQuery?
Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.
Getting data out of Recurly
Recurly uses a REST API to allow developers to get data out of the service. The API supports endpoints for billing information, coupons, plans, invoices, and more.
To get a list of Recurly accounts for a given subdomain, you could call
GET /v2/accounts, with any of seven optional parameters for selecting and sorting the output.
Sample Recurly data
Results of Recurly API calls are returned as XML files. An XML file returned from a "list accounts" call to the Recurly API might look like this:
<account href="https://your-subdomain.recurly.com/v2/accounts/1"> <adjustments href="https://your-subdomain.recurly.com/v2/accounts/1/adjustments"/> <billing_info href="https://your-subdomain.recurly.com/v2/accounts/1/billing_info"/> <invoices href="https://your-subdomain.recurly.com/v2/accounts/1/invoices"/> <redemptions href="https://your-subdomain.recurly.com/v2/accounts/1/redemptions"/> <subscriptions href="https://your-subdomain.recurly.com/v2/accounts/1/subscriptions"/> <transactions href="https://your-subdomain.recurly.com/v2/accounts/1/transactions"/> <account_code>1</account_code> <state>active</state> <username>verena1234</username> <email>email@example.com</email> <cc_emails>firstname.lastname@example.org,email@example.com</cc_emails> <first_name>Verena</first_name> <last_name>Example</last_name> <company_name>New Company Name</company_name> <vat_number nil="nil"/> <tax_exempt type="boolean">false</tax_exempt> <address> <address1>123 Main St.</address1> <address2 nil="nil"/> <city>Philadelphia</city> <state>PA</state> <zip>19107</zip> <country>US</country> <phone nil="nil"/> </address> <accept_language nil="nil"/> <has_live_subscription type="boolean">true</has_live_subscription> <has_active_subscription type="boolean">true</has_active_subscription> <has_future_subscription type="boolean">false</has_future_subscription> <has_canceled_subscription type="boolean">false</has_canceled_subscription> <has_past_due_invoice type="boolean">false</has_past_due_invoice> <hosted_login_token>96e74bd5e14d18e6da463a0d638a2621</hosted_login_token> <created_at type="datetime">2017-12-08T20:59:43Z</created_at> <updated_at type="datetime">2017-12-11T17:56:24Z</updated_at> <closed_at nil="nil"/> </account>
Preparing Recurly 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. Recurly's 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 Google BigQuery
Google Cloud Platform provides a guide you can follow when you begin loading data into BigQuery. Use the
bq command-line tool, and in particular the
bq load command, to upload data. The syntax is documented in the Quickstart guide for bq. You can supply the table or partition schema, or, for supported data formats, you can use schema auto-detection. Iterate through this process as many times as it takes to load all of your tables and table data into BigQuery.
Keeping Recurly 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 Recurly.
And remember, as with any code, once you write it, you have to maintain it. If Recurly modifies its API, or 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
BigQuery 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, PostgreSQL, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To Postgres, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.
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 move data from Recurly to Google BigQuery automatically. With just a few clicks, Stitch starts extracting your Recurly data, structuring it in a way that's optimized for analysis, and inserting that data into your Google BigQuery data warehouse.