Zuora to Snowflake

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

Zuora provides billing automation, commerce, and analytics applications for businesses with a service subscription model.

What is Snowflake?

Snowflake is a cloud-based data warehouse service that runs on Amazon Web Services using EC2 and S3 instances. Snowflake is designed to be fast, flexible, and easy to work with. For instance, for query processing, Snowflake creates virtual warehouses that run on separate compute clusters, so querying one virtual warehouse doesn't slow down the others.

Getting data out of Zuora

Zuora provides both a REST API and an older SOAP API that let you extract information from its system. If, for example, you wanted to retrieve a list of accounting codes, you could use the REST API to call GET /rest/v1/accounting-codes.

Sample Zuora data

The Zuora API returns data in JSON format. For example, the result of a call to retrieve a list of accounting codes might look like this:

    "accountingCodes": [
            "id": "e20b0747478025a10147816ba1c20097",
            "name": "Accounts Receivable",
            "type": "AccountsReceivable",
            "glAccountName": null,
            "glAccountNumber": null,
            "notes": null,
            "category": "Assets",
            "status": "Active",
            "CustomField__c": null,
            "createdOn": "2017-07-29 02:20:20",
            "createdBy": "e20b074746ec48f40147140f51e30a1a",
            "updatedOn": "2017-07-29 02:20:20",
            "updatedBy": "e20b074746ec48f40147140f51e30a1a"
            "id": "e20b0747478025a10147816ba21900a0",
            "name": "Discounts",
            "type": "SalesDiscounts",
            "glAccountName": null,
            "glAccountNumber": null,
            "notes": null,
            "category": "Revenue",
            "status": "Inactive",
            "CustomField__c": null,
            "createdOn": "2017-07-29 02:20:20",
            "createdBy": "e20b074746ec48f40147140f51e30a1a",
            "updatedOn": "2017-09-27 22:11:07",
            "updatedBy": "e20b074746ec48f40147140f51e30a1a"
    "success": true

Preparing Zuora 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.

Preparing data for Snowflake

Depending on your data structures, you may need to prepare your data before loading. Check the supported data types for Snowflake and make sure that your data maps neatly to them.

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

Loading data into Snowflake

Turn to Snowflake's Data Loading Overview for help with the task of loading your data. If you're not loading a lot of data, you might be able to use Snowflake's data loading wizard, but its limitations make it unsuitable as a reliable ETL solution for some use cases. As an alternative, you can:

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

You’ll have the option of copying from your local drive or from Amazon S3 – and Snowflake lets you make a virtual warehouse to power the insertion process.

Keeping Zuora 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 Zuora.

And remember, as with any code, once you write it, you have to maintain it. If Zuora 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, PostgreSQL, 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 BigQuery, To Postgres, 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 Zuora to Snowflake automatically. With just a few clicks, Stitch starts extracting your Zuora data, structuring it in a way that's optimized for analysis, and inserting that data into your Snowflake data warehouse.