Codat to Delta Lake

This page provides you with instructions on how to extract data from Codat and load it into Delta Lake. (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 Codat?

Codat provides a standardized data format across multiple accountancy software applications and financial APIs.

What is Delta Lake?

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.

Getting data out of Codat

Codat exposes data through a REST API, which developers can use to extract information. To access information, you can use a GET method to retrieve data via the Codat API. Codat exposes data from customers, suppliers, invoices, bills, payments, creditNotes, and bankStatements endpoints. You can use an optional query parameter in the format [propertyName][operator][value] to select only certain data. Operators include "equal to" (%3d) and, for numeric and date values, "greater than" (%3e), and "less than" (%ec). So, to retrieve invoices for a customer whose ID is "61," you would call:

GET /companies/[companyId]/data/invoices?query=customerRef.id%3d61

Sample Codat data

A GET call returns a JSON object with all the fields of the specified dataset as a reply. Invoices, for example, have 11 possible properties, though all of them may not be present for any given record, so the JSON might look like:

{
    "id": "20",
    "invoiceNumber": "1001",
    "customerRef": {
      "id": "55",
      "companyName": "Oxon - Holiday Party"
    },
    "issueDate": "2017-01-24T00:00:00",
    "dueDate": "2017-02-23T00:00:00",
    "currency": "GBP",
    "totalAmount": 10800,
    "amountDue": 0
}

Loading data into Delta Lake on Databricks

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.

Keeping Codat 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 Codat.

And remember, as with any code, once you write it, you have to maintain it. If Codat 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

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.

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 Codat to Delta Lake automatically. With just a few clicks, Stitch starts extracting your Codat data, structuring it in a way that's optimized for analysis, and inserting that data into your Delta Lake data warehouse.