This page provides you with instructions on how to extract data from QuickBooks and analyze it in Grafana. (If the mechanics of extracting data from QuickBooks seem too complex or difficult to maintain, 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 Grafana?
Grafana is an open source platform for time series analytics. It can run on-premises on all major operating systems or be hosted by Grafana Labs via GrafanaCloud. Grafana allows users to create, explore, and share dashboards to query, visualize, and alert on data.
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 Grafana
Analyzing data in Grafana requires putting it into a format that Grafana can read. Grafana natively supports nine data sources, and offers plugins that provide access to more than 50 more. Generally, it's a good idea to move all your data into a data warehouse for analysis. MySQL, Microsoft SQL Server, and PostgreSQL are among the supported data sources, and because Amazon Redshift is built on PostgreSQL and Panoply is built on Redshift, those popular data warehouses are also supported. However, Snowflake and Google BigQuery are not currently supported.
Analyzing data in Grafana
Grafana provides a getting started guide that walks new users through the process of creating panels and dashboards. Panel data is powered by queries you build in Grafana's Query Editor. You can create graphs with as many metrics and series as you want. You can use variable strings within panel configuration to create template dashboards. Time ranges generally apply to an entire dashboard, but you can override them for individual panels.
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.
From QuickBooks to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing QuickBooks data in Grafana is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites QuickBooks to Redshift, QuickBooks to BigQuery, QuickBooks to Azure SQL Data Warehouse, QuickBooks to PostgreSQL, QuickBooks to Panoply, and QuickBooks to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data from QuickBooks to Grafana automatically. With just a few clicks, Stitch starts extracting your QuickBooks data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Grafana.