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

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

Connecting to CloudConvert Data

Connecting to CloudConvert 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.

CloudConvert uses API key authentication. Your CloudConvert API key is used to authenticate requests as a Bearer token. You can generate or view your keys at https://cloudconvert.com/dashboard/api/v2/keys.

Using API Key Authentication

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your CloudConvert API key.

Example connection string:

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

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

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

Create a SQL Statement to Query CloudConvert

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

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

Extract, Transform, and Load the CloudConvert Data

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

Loading CloudConvert Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

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

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

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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