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

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

Connecting to Bitly Data

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

Bitly API Profile Settings

Log into your Bitly account, go to Settings > Developer Settings, and generate a Generic Access Token.

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

cnxn = mod.connect("Profile=C:\profiles\Bitly.apip;ProfileSettings='APIKey=your_access_token';")

Create a SQL Statement to Query Bitly

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

sql = "SELECT City, Clicks FROM BitlinkCities WHERE Country = 'United States'"

Extract, Transform, and Load the Bitly Data

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

Loading Bitly Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Bitly 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 Bitly 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\Bitly.apip;ProfileSettings='APIKey=your_access_token';")

sql = "SELECT City, Clicks FROM BitlinkCities WHERE Country = 'United States'"

table1 = etl.fromdb(cnxn,sql)

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

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