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

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.

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.

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
}

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.

Google Cloud Platform offers a helpful guide you can follow to begin loading data into BigQuery. Use the bq command-line tool, and in particular the bq load command, to upload files to your datasets. 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 into BigQuery.

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.

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.