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.)
What is QuickBooks?
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.
What is Snowflake?
Snowflake is a cloud-based data warehouse implemented as a managed service running on Amazon Web Services EC2 and S3 instances. Snowflake separates compute and storage resources, enabling users to scale the two independently and pay only for resources used. It provides native support for JSON, Avro, XML, and Parquet data, and can provide access to the same data for multiple workgroups or workloads simultaneously with no contention roadblocks or performance degradation.
Getting data out of QuickBooks
Sample QuickBooks data
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>
Preparing data for Snowflake
Depending on the structure of your data, you may need to prepare it for loading. Look at the supported data types for Snowflake and make sure that the data you've got will map neatly to them.
Note that you don't need to define a schema in advance when loading JSON data into Snowflake.
Loading data into Snowflake
Snowflake's documentation outlines a Data Loading Overview that can help you with the task of loading your data. If you're not loading a lot of data, look into the data loading wizard in the Snowflake web UI, but for many organizations, the limitations on that tool will make it a non-starter as a reliable ETL solution. Instead:
- Use the PUT command to stage files.
- Use the COPY INTO table command to load prepared data into an awaiting table.
You can copy from your local drive or from Amazon S3. Snowflake lets you make a virtual warehouse that can power the insertion process.
Keeping QuickBooks data up to date
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.
Other data warehouse options
Snowflake 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 Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. 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 Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.
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 QuickBooks to Snowflake automatically. With just a few clicks, Stitch starts extracting your QuickBooks data, structuring it in a way that's optimized for analysis, and inserting that data into your Snowflake data warehouse.