QuickBooks to Looker

This page provides you with instructions on how to extract data from QuickBooks and analyze it in Looker. (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 Looker?

Looker is a powerful, modern business intelligence platform that has become the new standard for how modern enterprises analyze their data. From large corporations to agile startups, savvy companies can leverage Looker's analysis capabilities to monitor the health of their businesses and make more data-driven decisions.

Looker is differentiated from other BI and analysis platforms for a number of reasons. Most notable is the use of LookML, a proprietary language for describing dimensions, aggregates, calculations, and data relationships in a SQL database. LookML enables organizations to abstract the query logic behind their analyses from the content of their reports, making their analytics easy to manage, evolve, and scale.

Getting data out of QuickBooks

To load QuickBooks data to a data warehouse, first pull the data off of QuickBooks' servers using the QuickBooks Accounting and Payments APIs, which are discussed in the QuickBooks programming guide.

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 Looker

To perform its analyses, Looker connects to your company's database or data warehouse, where the data you want to analyze is stored. Some popular data warehouses include Amazon Redshift, Google BigQuery, and Snowflake.

Looker's documentation offers instructions on how to configure and connect your data warehouse. In most cases, it's simply a matter of creating and copying access credentials, which may include a username, password, and server information. You can then move data from your various data sources into your data warehouse for Looker to use.

Analyzing data in Looker

Once your data warehouse is connected to Looker, you can build constructs known as explores, each of which is a SQL view containing a specific set of data for analysis. An example might be "orders" or "customers."

Once you've selected any given explore, you can filter data based on any column available in the view, group data based on certain fields in the view (known as dimensions), calculate outputs such as sums and counts (known as measures), and pick a visualization type such as a bar chart, pie chart, map, or bubble chart.

Beyond this simple use case, Looker offers a broad universe of functionality that allows you to conduct analyses and share them with your organization. You can get started with this walkthrough in Looker's documentation.

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 Looker 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, and QuickBooks to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to solve this problem 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 Looker.