How to Build an ETL App for Qualaroo Data in Python with CData

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

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

Connecting to Qualaroo Data

Connecting to Qualaroo 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.

Qualaroo uses HTTP Basic Authentication to control access to the API. You will need your API Key and API Secret, which can be found under Account Details > Reporting API in the Qualaroo dashboard.

Using Basic Authentication

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to Basic.
• User: Set this to your Qualaroo API Key.
• Password: Set this to your Qualaroo API Secret.

Example connection string:

Profile=C:\profiles\Qualaroo.apip;AuthScheme=Basic;User=your_api_key;Password=your_api_secret;

After installing the CData Qualaroo Connector, follow the procedure below to install the other required modules and start accessing Qualaroo 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 Qualaroo 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.api as mod

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

cnxn = mod.connect("Profile=C:\profiles\Qualaroo.apip;AuthScheme=Basic;User=your_api_key;Password=your_api_secret;")

Create a SQL Statement to Query Qualaroo

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

sql = "SELECT ,  FROM SurveyResponses WHERE SurveyId = '12345'"

Extract, Transform, and Load the Qualaroo Data

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

Loading Qualaroo Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Qualaroo 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 CData API Driver for Python to start building Python apps and scripts with connectivity to Qualaroo 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.api as mod

cnxn = mod.connect("Profile=C:\profiles\Qualaroo.apip;AuthScheme=Basic;User=your_api_key;Password=your_api_secret;")

sql = "SELECT ,  FROM SurveyResponses WHERE SurveyId = '12345'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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