How to Build an ETL App for Bing Search Results 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 Python Connector for Bing Search and the petl framework, you can build Bing Search-connected applications and pipelines for extracting, transforming, and loading Bing Search results. This article shows how to connect to Bing Search with the CData Python Connector and use petl and pandas to extract, transform, and load Bing Search results.

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

Connecting to Bing Search Results

Connecting to Bing Search results 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.

To connect to Bing, set the ApiKey connection property. To obtain the API key, sign into Microsoft Cognitive Services and register for the Bing Search APIs.

Two API keys are then generated; select either one.

When querying tables, the SearchTerms parameter must be supplied in the WHERE clause.

After installing the CData Bing Search Connector, follow the procedure below to install the other required modules and start accessing Bing Search 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 Bing Search Results 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.bing as mod

You can now connect with a connection string. Use the connect function for the CData Bing Search Connector to create a connection for working with Bing Search results.

cnxn = mod.connect("APIKey=MyAPIKey;")

Create a SQL Statement to Query Bing Search

Use SQL to create a statement for querying Bing Search. In this article, we read data from the VideoSearch entity.

sql = "SELECT Title, ViewCount FROM VideoSearch WHERE SearchTerms = 'WayneTech'"

Extract, Transform, and Load the Bing Search Results

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

Loading Bing Search Results into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for Bing Search, you can work with Bing Search results 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 Python Connector for Bing Search to start building Python apps and scripts with connectivity to Bing Search results. Reach out to our Support Team if you have any questions.

Full Source Code

import petl as etl
import pandas as pd
import cdata.bing as mod

cnxn = mod.connect("APIKey=MyAPIKey;")

sql = "SELECT Title, ViewCount FROM VideoSearch WHERE SearchTerms = 'WayneTech'"

table1 = etl.fromdb(cnxn,sql)

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

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