This page provides you with instructions on how to extract data from Quickbooks 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.)
Snowflake is a data warehouse solution that is entirely cloud based. It's a managed service. If you don't want to deal with hardware, software, or upkeep for a data warehouse you're going to love Snowflake. It runs on the wicked fast Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be flexible and easy to work with where other relational databases are not. One example of this is the query execution. Snowflake creates virtual warehouses where query processing takes place. These virtual warehouses run on separate compute clusters, so querying one of these virtual warehouses doesn't slow down the others. If you have ever had to wait for a query to complete, you know the value of speed and efficiency for query processing.
Getting data out of QuickBooks
If our goal is to load QuickBooks data to a data warehouse, the first step is pulling that data off of Quickbooks’ servers. You can do this using the Quickbooks Accounting and Payments API’s which are available to everyone who uses the service. The full programming guide can be accessed here.
Sample QuickBooks data
The API returns XML-formatted data. Below is an example of the kind of response you might see when querying the api.
<div class="codeDiv"> <pre> <code><p><IntuitResponse xmlns="http://schema.intuit.com/finance/v3" time="2013-04-03T10:22:55.766Z"> <QueryResponse startPosition="10" maxResults="2"> <Customer> <Id>2123</Id> <SyncToken>0</SyncToken> ... <GivenName>Srini</GivenName> </Customer> <Customer> <Id>2124</Id> <SyncToken>0</SyncToken> ... <GivenName>Peter</GivenName> </Customer> </QueryResponse> </IntuitResponse> </p></code> </pre> </div>
Preparing data for Snowflake
Depending on the structure that you data is in, you may need to prepare it for loading. Take a look at the supported data types for Snowflake and make sure that the data you've got will map neatly to them. If you have a lot of data, you should compress it. Gzip, bzip2, Brotli, Zstandard v0.8 and deflate/raw deflate compression types are all supported.
One important thing to note here is that you don't need to define a schema in advance when loading JSON data into Snowflake. Onward to loading!
Loading data into Snowflake
Keeping QuickBooks data up to date
Ok, this is great! You’ve developed a script that pulls data from QuickBooks and loads it into Redshift, but what happens tomorrow when you have new transactions, invoices, payments and whatever else?
The key is to build your script in such a way that it can also identify incremental updates to your data. Some API’s include fields like ‘time’ that allow you to quickly identify records that are new since your last update (or since the newest record you’ve copied). You can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.
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 Quickbooks data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Snowflake data warehouse.