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

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

Connecting to ScrapingBee Data

Connecting to ScrapingBee 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

ScrapingBee uses API key authentication. To obtain an API key:

1. Sign in to your ScrapingBee account at https://app.scrapingbee.com
2. Navigate to the Dashboard and locate your API key in the top section.
3. Copy the API key for use in the connection string.

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 ScrapingBee API key.

Example Connection String

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

Connecting to ScrapingBee

Once the authentication is configured, you can connect to ScrapingBee and query data from any of the available tables. All tables require at least one input parameter (such as a search query or product ID) to retrieve data.

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

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

Create a SQL Statement to Query ScrapingBee

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

sql = "SELECT ,  FROM GoogleSearchResults WHERE SearchQuery = 'cdata drivers'"

Extract, Transform, and Load the ScrapingBee Data

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

Loading ScrapingBee Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with ScrapingBee 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 ScrapingBee data. Reach out to our Support Team if you have any questions.

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Full Source Code

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

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

sql = "SELECT ,  FROM GoogleSearchResults WHERE SearchQuery = 'cdata drivers'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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