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

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

Connecting to Browserless Data

Connecting to Browserless 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.

Browserless uses HTTP API token authentication. Your Browserless API token is sent as the token query parameter on every request. You can generate or view your token in the Browserless dashboard at https://account.browserless.io/.

Using ApiKey Authentication

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Browserless API token.

Example connection string:

Profile=C:\profiles\Browserless.apip;AuthScheme=APIKey;ProfileSettings="ApiKey=your_api_token";

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

cnxn = mod.connect("Profile=C:\profiles\Browserless.apip;AuthScheme=APIKey;ProfileSettings="ApiKey=your_api_token";")

Create a SQL Statement to Query Browserless

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

sql = "SELECT ,  FROM Map WHERE Url = 'https://www.example.com'"

Extract, Transform, and Load the Browserless Data

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

Loading Browserless Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

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

sql = "SELECT ,  FROM Map WHERE Url = 'https://www.example.com'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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