This page provides you with instructions on how to extract data from Quick Base and analyze it in Metabase. (If the mechanics of extracting data from Quick Base 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 Quick Base?
"Quick Base is a low-code database and application development platform. It lets teams work with a common data repository to build forms, create reports, set up workflows, and automate processes. And its low-code capability enables non-developers, sometimes called citizen developers, to create applications without having to request time and attention from the IT department." (Source: Computerworld)
What is Metabase?
Metabase provides a visual query builder that lets users generate simple charts and dashboards, and supports SQL for gathering data for more complex business intelligence visualizations. It runs as a JAR file, and its developers make it available in a Docker container and on Heroku and AWS. Metabase is free of cost and open source, licensed under the AGPL.
Getting data out of Quick Base
Quick Base has a REST API that developers can use to get at information stored in the platform. The API relies on XML for data interchange. To get information about a database by name, you would create and post an XML file like this:
<qdbapi> <ticket>auth_ticket</ticket> <dbname>TestTable</dbname> </qdbapi>
Sample Quick Base data
Here's an example of the kind of response you might see with a query like the one above.
<?xml version="1.0" ?> <qdbapi> <action>API_FindDBByName</action> <errcode>0</errcode> <errtext>No error</errtext> <dbid>bdcagynhs</dbid> </qdbapi>
Preparing Quick Base data
If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Quick Base's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.
Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.
Loading data into Metabase
Metabase works with data in databases; you can't use it as a front end for a SaaS application without replicating the data to a data warehouse first. Out of the box Metabase supports 15 database sources, and you can download 10 additional third-party database drivers, or write your own. Once you specify the source, you must specify a host name and port, database name, and username and password to get access to the data.
Using data in Metabase
Metabase supports three kinds of queries: simple, custom, and SQL. Users create simple queries entirely through a visual drag-and-drop interface. Custom queries use a notebook-style editor that lets users select, filter, summarize, and otherwise customize the presentation of the data. The SQL editor lets users type or paste in SQL queries.
Keeping Quick Base data up to date
At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.
Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Quick Base. And remember, as with any code, once you write it, you have to maintain it. If Quick Base modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.
From Quick Base to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Quick Base data in Metabase 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 Quick Base to Redshift, Quick Base to BigQuery, Quick Base to Azure SQL Data Warehouse, Quick Base to PostgreSQL, Quick Base to Panoply, and Quick Base to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Quick Base with Metabase. With just a few clicks, Stitch starts extracting your Quick Base data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Metabase.