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

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

Connecting to Bright Data Data

Connecting to Bright Data 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

To use the Bright Data API, you need an API key from the Bright Data Control Panel. Navigate to Account Settings > API to generate or retrieve your API key.

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Bright Data API key from the Control Panel.

Example connection string:

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

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

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

Create a SQL Statement to Query Bright Data

Use SQL to create a statement for querying Bright Data. In this article, we read data from the AccountStatus entity.

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

Extract, Transform, and Load the Bright Data Data

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

Loading Bright Data Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

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

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

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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