QuickBooks is Intuit's accounting software, which is available in both Desktop and Online editions. Targeted at small and medium-sized businesses, it manages payroll, inventory, and sales, and includes marketing tools, merchant services, and training resources.

Amazon S3 (Simple Storage Service) provides cloud-based object storage through a web service interface. You can use S3 to store and retrieve any amount of data, at any time, from anywhere on the web. S3 objects, which may be structured in any way, are stored in resources called buckets.

To load QuickBooks data to a data warehouse, first pull the data off of QuickBooks' servers using the QuickBooks Accounting and Payments APIs, which are discussed in the QuickBooks programming guide.

QuickBooks' APIs return XML-formatted data, as in this example.

<IntuitResponse xmlns="http://schema.intuit.com/finance/v3" time="2017-04-03T10:22:55.766Z">
 <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>

To upload files you must first create an S3 bucket. Once you have a bucket you can add an object to it. An object can be any kind of file: a text file, data file, photo, or anything else. You can optionally compress or encrypt the files before you load them.

It's great that you've developed a script that pulls data from QuickBooks and loads it into a data warehouse, but what happens when you have new transactions, invoices, and payments?

The key is to build your script in such a way that it can identify incremental updates to your data. Use fields like CreateTime and LastUpdatedTime to identify records that are new since your last update, or since the most recent record you copied. Once you've taken new data into account, you can set up your script as a cron job or continuous loop to keep pulling down new data as it appears.

S3 is great, but sometimes you want a more structured repository that can serve as a basis for BI reports and data analytics — in short, a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, Microsoft Azure Synapse Analytics, or Panoply, which are RDBMSes that use similar SQL syntax. 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 Azure Synapse Analytics, and To Panoply.