Recurly to Snowflake

This page provides you with instructions on how to extract data from Recurly and load it into Snowflake. (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 Snowflake?

Snowflake is a cloud-based data warehouse that runs on Amazon Web Services EC2 and S3 instances. Snowflake can natively load and optimize both structured and semi-structured data and make it available via SQL. As a managed service, it's easy to work with, and its columnar database engine, running on the scalable AWS platform, makes it fast.

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>verena@example.com</email>
  <cc_emails>bob@example.com,susan@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.

Preparing data for Snowflake

Depending on how your data is structured, you may need to prepare it for loading. Read about the supported data types for Snowflake and make sure that your data maps well to them.

Note that you don't need to define a schema in advance when loading JSON data into Snowflake.

Loading data into Snowflake

Snowflake's Data Loading Overview documentation can help you with loading your data. If you're not loading a lot of data, you might be able to use the data loading wizard in the Snowflake web UI, but chances are that that tool's limitations will make it unsuitable as a reliable ETL solution. Another approach involves two steps for getting data into Snowflake:

  • Use the PUT command to stage files.
  • Use the COPY INTO table command to load prepared data into an awaiting table.

You can copy the data from your local drive or from Amazon S3. Snowflake lets you make a virtual warehouse that can power the insertion process.

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

Snowflake 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, or PostgreSQL, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. 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 Panoply.

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