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

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

Connecting to PDFMonkey Data

Connecting to PDFMonkey 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.

Using API Key Authentication

PdfMonkey uses API key authentication. To obtain an API key:

1. Log in to your PdfMonkey account at https://app.pdfmonkey.io
2. Navigate to your account settings
3. Open the API Key page
4. Copy your API key

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your PdfMonkey API key.

Example Connection String

Profile=C:\profiles\PdfMonkey.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key"

Connecting to PdfMonkey

Once the authentication is configured, you can connect to PdfMonkey and query data from any of the available tables such as CurrentUser, DocumentCards, Documents, DocumentTemplateCards, and DocumentTemplates.

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

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

Create a SQL Statement to Query PDFMonkey

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

sql = "SELECT ,  FROM CurrentUser WHERE  = ''"

Extract, Transform, and Load the PDFMonkey Data

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

Loading PDFMonkey Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with PDFMonkey 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 PDFMonkey data. Reach out to our Support Team if you have any questions.

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Full Source Code

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

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

sql = "SELECT ,  FROM CurrentUser WHERE  = ''"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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