How to load data from the QuickBooks to Snowflake

Access your data on QuickBooks

The first step in loading your QuickBooks data into any data warehouse solution is to access them and extract it through the available web API.

QuickBooks has a very rich and well-defined API, reflecting the extensive development that the product has gone through. The API is designed around the following main groups of resources.

1. Transaction resources.
2. Name list resources.
3. Report resources.
4. Supporting resources.

Report resources contain all the reports that QuickBooks also offer from within the application. They have a different data model than the rest of the resources, and you need to account for these differences when extracting data from the API.

The rest of the resources contain pretty much every possible entity that QuickBooks defines, each with a different data model serialized in JSON.

In addition to the above, the things that you have to keep in mind when dealing with any API like the one Quickbooks has, are:

1. Rate limits. Every API has some rate limits that you have to respect.
2. Authentication. You authenticate on QuickBooks using an API key.
3. Paging and dealing with a big amount of data. Platforms like QuickBooks tend to generate a lot of data, as financial transactions and accounting involve many different events that can happen. Pulling big volumes of data out of an API might be difficult, especially when you consider and respect any rate limits that the API has.

Transform and prepare your QuickBooks data for Snowflake

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 is going to be used
2. The type of analysis that you plan to perform

Each system has specific limitations on any data types and data structures that it supports. If you want to push data into Google BigQuery, 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 it into a database.

Also, you have to choose the right data types. Again, depending on the system that you will send 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 table.

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 in a database.

Data in Snowflake DB is organized around tables with a well-defined set of columns with each one having a specific data type.

Snowflake supports a rich set of data types. It is worth mentioning that a number of semi-structured data types are also supported. It is possible to load data directly in JSON, Avro, ORC, Parquet, or XML format with Snowflake. Hierarchical data is treated as a first-class citizen, similar to what Google BigQuery offers.

There is also one notable common data type that Snowflake does not support. LOB or large object data type is not supported. Instead, you should use a BINARY or VARCHAR type instead. But these types are not that useful for data warehouse use cases.

A typical strategy for loading data from Quickbooks to Snowflake is to create a schema where you will map each API endpoint to a table.

Each key inside the Quickbooks API endpoint response should be mapped to a table column, and you should ensure the right conversion to a Snowflake DB data type.

Of course, you will need to ensure that as data types from the Quickbooks API might change, you will adapt your database tables accordingly. There’s no such thing as automatic data type casting.

After you have a complete and well-defined data model or schema for Snowflake DB, you can move forward and start loading your data into the database.

Load data from Quickbooks to Snowflake

In JSON format, files containing data are stored in a local file system or Amazon S3 buckets. Usually, data is loaded into Snowflake DB in a bulk way, using the COPY INTO command. Then a COPY INTO command is invoked on the Snowflake DB instance, and data is copied into the data warehouse.

The files can be pushed into Snowflake using the PUT command into a staging environment before the COPY command is invoked.

Another alternative is to upload data directly into a service like Amazon S3 from where Snowflake can access data directly.

Updating your Quickbooks data on Snowflake

As you will be generating more data on Quickbooks, you will need to update your older data on Snowflake DB. This includes new records and updates to older records that have been updated on Quickbooks for any reason.

You will need to periodically check Quickbooks for new data and repeat the process that has been described previously while updating your currently available data if needed. Updating an already existing row on a Snowflake DB table is achieved by creating UPDATE statements.

Another issue that you need to take care of is identifying and removing any duplicate records on your database. Either because Quickbooks does not have a mechanism to identify new and updated records or because of errors on your data pipelines, duplicate records might be introduced to your database.

In general, ensuring the quality of data that is inserted into your database is a big and difficult issue.

The best way to load data from QuickBooks to Snowflake

So far we just scraped the surface of what you can do with Snowflake DB and how to load data into it. Things can get even more complicated if you want to integrate data coming from different sources.

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