This page provides you with instructions on how to extract data from QuickBooks and load it into Panoply. (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 Panoply?
Panoply can spin up a new Amazon Redshift instance in just a few clicks. Panoply's managed data warehouse service uses machine learning and natural language processing (NLP) to learn, model, and automate data management activities from source to analysis. It can import data with no schema, no modeling, and no configuration, and lets you use analysis, SQL, and visualization tools just as you would if you were creating a Redshift data warehouse on your own.
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>
Loading data into Panoply
Once you know all of the columns you want to insert, use the CREATE TABLE statement in Panoply's Redshift data warehouse to set up a table to receive all the data.
Next, migrate your data. It may seem like the easiest course would be to build INSERT statements to add data to your Redshift table row by row. That would be a mistake; Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, a better approach is to copy the data into Amazon S3 and then use the COPY command to load it into Redshift.
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
Panoply 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, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax. 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 Snowflake, To Azure SQL Data Warehouse, 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 Panoply 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 Panoply data warehouse.