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

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

Connecting to ZeroBounce Data

Connecting to ZeroBounce 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

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

1. Log in to your ZeroBounce account at https://app.zerobounce.net
2. Navigate to the API tab in your account dashboard
3. Copy the API key displayed on the page

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 ZeroBounce API key.

Example Connection String

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

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

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

Create a SQL Statement to Query ZeroBounce

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

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

Extract, Transform, and Load the ZeroBounce Data

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

Loading ZeroBounce Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

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

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

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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