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

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

Connecting to Browse AI Data

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

BrowseAI API Profile Settings

BrowseAI is a web scraping and automation platform that allows you to train robots to extract and monitor data from websites. Authentication is performed using an API key that is sent as a Bearer token in the Authorization header.

The BrowseAI API key has the format {userId}:{apiKey}, where both parts are UUIDs separated by a colon. This full string is provided as a single value in the BrowseAI dashboard.

To obtain your API key:

1. Log in to your BrowseAI account at https://app.browse.ai
2. Navigate to Settings in the dashboard
3. Locate the API section
4. Copy the full API key string displayed (format: userId:apiKey)

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 BrowseAI API key (format: userId:apiKey).

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

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

Create a SQL Statement to Query Browse AI

Use SQL to create a statement for querying Browse AI. In this article, we read data from the RobotTasks entity.

sql = "SELECT Id, Status FROM RobotTasks WHERE RobotId = 'your-robot-id'"

Extract, Transform, and Load the Browse AI Data

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

Loading Browse AI Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, Status FROM RobotTasks WHERE RobotId = 'your-robot-id'"

table1 = etl.fromdb(cnxn,sql)

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

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