After you have accessed your data on QuickBooks, you will have to transform it based on two main factors,
1. The limitations of the database that the data will be loaded onto
2. The type of analysis that you plan to perform
Each system has specific limitations on the data types and data structures that it supports. If for example you want to push data into Google BigQuery, then you can send nested data like JSON directly. But when you are dealing with tabular data stores, like Microsoft SQL Server, this is not an option. Instead, you will have to flatten out your data before loading into the database.
Also, you have to choose the right data types. Again, depending on the system that you will send the data to and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit the expressivity of your queries and limit your analysts on what they can do directly out of the database.
QuickBooks has a very rich data model, where many of the resources that you can access might have to flatten out and be pushed in more than one tables.
Also, QuickBooks has a special set of resources, the reports, that have a tabular but nested format that looks similar to a complex spreadsheet. In order to make these reports compatible with a database data model, you need to redesign, parse and transform the reports into a tabular form that can be stored into a database.