How to Build an ETL App for Klipfolio 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 Klipfolio-connected applications and pipelines for extracting, transforming, and loading Klipfolio data. This article shows how to connect to Klipfolio with the CData Python Connector and use petl and pandas to extract, transform, and load Klipfolio data.

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

Connecting to Klipfolio Data

Connecting to Klipfolio 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.

Start by setting the Profile connection property to the location of the Klipfolio Profile on disk (e.g. C:\profiles\Klipfolio.apip). Next, set the ProfileSettings connection property to the connection string for Klipfolio (see below).

Klipfolio API Profile Settings

In order to authenticate to Klipfolio, you'll need to provide your API Key. You can generate an API key from the Klipfolio Dashboard app through either the My Profile page or from Users if you are an administrator (you must have the user.manage permission). Set the API Key in the ProfileSettings property to connect.

After installing the CData Klipfolio Connector, follow the procedure below to install the other required modules and start accessing Klipfolio 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 Klipfolio 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 Klipfolio Connector to create a connection for working with Klipfolio data.

cnxn = mod.connect("Profile=C:\profiles\Klipfolio.apip;ProfileSettings='APIKey=your_api_key';")

Create a SQL Statement to Query Klipfolio

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

sql = "SELECT Id, Name FROM DataSources WHERE IsDynamic = 'true'"

Extract, Transform, and Load the Klipfolio Data

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

Loading Klipfolio Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Klipfolio 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 Klipfolio 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\Klipfolio.apip;ProfileSettings='APIKey=your_api_key';")

sql = "SELECT Id, Name FROM DataSources WHERE IsDynamic = 'true'"

table1 = etl.fromdb(cnxn,sql)

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

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