Invoiced provides accounts receivable automation software that helps companies get paid faster, waste less time on collections, and provide a better customer experience. It automates billing tasks like sending out invoices, following up with late-paying customers, and reconciling incoming invoice payments. It also help businesses handle recurring billing and payment plans.

Delta Lake is an open source storage layer that sits on top of existing data lake file storage, such AWS S3, Azure Data Lake Storage, or HDFS. It uses versioned Apache Parquet files to store data, and a transaction log to keep track of commits, to provide capabilities like ACID transactions, data versioning, and audit history.

Invoiced provides an API that lets developers get at customer, payment, invoicing, and other information stored in the platform. For example, to get information about a particular invoice, you would call GET /invoices/:{id}.

Here's an example of the kind of response you might see with a query like the one above.

{
  "id": 46225,
  "object": "invoice",
  "customer": 15444,
  "name": null,
  "currency": "usd",
  "draft": false,
  "closed": false,
  "paid": false,
  "status": "not_sent",
  "autopay": false,
  "attempt_count": 0,
  "next_payment_attempt": null,
  "subscription": null,
  "number": "INV-0016",
  "date": 1416290400,
  "due_date": 1417500000,
  "payment_terms": "NET 14",
  "items": [
    {
      "id": 7,
      "object": "line_item",
      "catalog_item": null,
      "type": "product",
      "name": "Copy Paper, Case",
      "description": null,
      "quantity": 1,
      "unit_cost": 45,
      "amount": 45,
      "discountable": true,
      "discounts": [],
      "taxable": true,
      "taxes": [],
      "metadata": {}
    },
    {
      "id": 8,
      "object": "line_item",
      "catalog_item": "delivery",
      "type": "service",
      "name": "Delivery",
      "description": null,
      "quantity": 1,
      "unit_cost": 10,
      "amount": 10,
      "discountable": true,
      "discounts": [],
      "taxable": true,
      "taxes": [],
      "metadata": {}
    }
  ],
  "notes": null,
  "subtotal": 55,
  "discounts": [],
  "taxes": [
    {
      "id": 20554,
      "object": "tax",
      "amount": 3.85,
      "tax_rate": null
    }
  ],
  "total": 51.15,
  "balance": 51.15,
  "url": "https://dundermifflin.invoiced.com/invoices/IZmXbVOPyvfD3GPBmyd6FwXY",
  "payment_url": "https://dundermifflin.invoiced.com/invoices/IZmXbVOPyvfD3GPBmyd6FwXY/payment",
  "pdf_url": "https://dundermifflin.invoiced.com/invoices/IZmXbVOPyvfD3GPBmyd6FwXY/pdf",
  "created_at": 1415229884,
  "metadata": {}
}

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. Invoiced'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. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

To create a Delta table, you can use existing Apache Spark SQL code and change the format from parquet, csv, or json to delta. Once you have a Delta table, you can write data into it using Apache Spark's Structured Streaming API. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table. Databricks provides quickstart documentation that explains the whole process.

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.

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Invoiced's API results include fields like created_at that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

Delta Lake on Databricks 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 Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3. 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 Snowflake, To Panoply, and To S3.