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

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

Connecting to ConvertAPI Data

Connecting to ConvertAPI 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.

ConvertAPI uses API Secret Key authentication. Your ConvertAPI Secret is used to authenticate requests. You can find your API Secret in the ConvertAPI dashboard under your account settings.

Using APIKey Authentication

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your ConvertAPI API secret key.

Example connection string:

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

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

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

Create a SQL Statement to Query ConvertAPI

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

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

Extract, Transform, and Load the ConvertAPI Data

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

Loading ConvertAPI Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

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

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

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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