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Extract, Transform, and Load QuickBase Data in Python

The CData Python Connector for QuickBase enables you to create ETL applications and pipelines for QuickBase data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for QuickBase and the petl framework, you can build QuickBase-connected applications and pipelines for extracting, transforming, and loading QuickBase data. This article shows how to connect to QuickBase with the CData Python Connector and use petl and pandas to extract, transform, and load QuickBase data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live QuickBase data in Python. When you issue complex SQL queries from QuickBase, the driver pushes supported SQL operations, like filters and aggregations, directly to QuickBase and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to QuickBase Data

Connecting to QuickBase data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

User Authentication Method

To authenticate with user credentials, specify the following connection properties:

  1. Set the User and Password.
  2. If your application requires an ApplicationToken;, you must provide it otherwise an error will be thrown. You can find the ApplicationToken under SpecificApp > Settings > App management > App properties > Advanced settings > Security options > Manage Application Token.

User Token Authentication

To authenticate with a user token, specify the following connection properties:

  1. Set UserToken and you are ready to connect. You can find the UserToken under Quick Base > My Preferences > My User Information > Manage User Tokens.

After installing the CData QuickBase Connector, follow the procedure below to install the other required modules and start accessing QuickBase through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for QuickBase Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.quickbase as mod

You can now connect with a connection string. Use the connect function for the CData QuickBase Connector to create a connection for working with QuickBase data.

cnxn = mod.connect("User=user@domain.com;Password=password;Domain=myinstance.quickbase.com;ApplicationToken=bwkxrb5da2wn57bzfh9xn24")

Create a SQL Statement to Query QuickBase

Use SQL to create a statement for querying QuickBase. In this article, we read data from the SampleTable_1 entity.

sql = "SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = '100'"

Extract, Transform, and Load the QuickBase Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the QuickBase data. In this example, we extract QuickBase data, sort the data by the Column1 column, and load the data into a CSV file.

Loading QuickBase Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Column1')

etl.tocsv(table2,'sampletable_1_data.csv')

In the following example, we add new rows to the SampleTable_1 table.

Adding New Rows to QuickBase

table1 = [ ['Id','Column1'], ['NewId1','NewColumn11'], ['NewId2','NewColumn12'], ['NewId3','NewColumn13'] ]

etl.appenddb(table1, cnxn, 'SampleTable_1')

With the CData Python Connector for QuickBase, you can work with QuickBase data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the QuickBase Python Connector to start building Python apps and scripts with connectivity to QuickBase data. Reach out to our Support Team if you have any questions.



Full Source Code


import petl as etl
import pandas as pd
import cdata.quickbase as mod

cnxn = mod.connect("User=user@domain.com;Password=password;Domain=myinstance.quickbase.com;ApplicationToken=bwkxrb5da2wn57bzfh9xn24")

sql = "SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = '100'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Column1')

etl.tocsv(table2,'sampletable_1_data.csv')

table3 = [ ['Id','Column1'], ['NewId1','NewColumn11'], ['NewId2','NewColumn12'], ['NewId3','NewColumn13'] ]

etl.appenddb(table3, cnxn, 'SampleTable_1')