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

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

Connecting to ZenRows Data

Connecting to ZenRows 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 ZenRows Profile on disk (e.g. C:\profiles\ZenRows.apip). Next, set the ProfileSettings connection property to the connection string for ZenRows (see below).

ZenRows API Profile Settings

To use the ZenRows API, you need to obtain an API key from your ZenRows account. Navigate to the ZenRows dashboard at app.zenrows.com and copy your API key from the account settings.

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

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

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

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

Create a SQL Statement to Query ZenRows

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

sql = "SELECT ProductId, ProductName FROM AmazonDiscovery WHERE Query = 'laptop'"

Extract, Transform, and Load the ZenRows Data

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

Loading ZenRows Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT ProductId, ProductName FROM AmazonDiscovery WHERE Query = 'laptop'"

table1 = etl.fromdb(cnxn,sql)

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

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