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

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

Connecting to Stack Exchange Data

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

Stack Exchange API Profile Settings

Register an application on StackApps to obtain an API Key. The Site property specifies which Stack Exchange site to query (e.g., stackoverflow).

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

cnxn = mod.connect("Profile=C:\profiles\StackExchange.apip;ProfileSettings='APIKey=your_api_key;Site=stackoverflow';")

Create a SQL Statement to Query Stack Exchange

Use SQL to create a statement for querying Stack Exchange. In this article, we read data from the Answers entity.

sql = "SELECT AnswerId, CreationDate FROM Answers WHERE IsAccepted = 'true'"

Extract, Transform, and Load the Stack Exchange Data

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

Loading Stack Exchange Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Stack Exchange 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 Stack Exchange 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\StackExchange.apip;ProfileSettings='APIKey=your_api_key;Site=stackoverflow';")

sql = "SELECT AnswerId, CreationDate FROM Answers WHERE IsAccepted = 'true'"

table1 = etl.fromdb(cnxn,sql)

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

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