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

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

Connecting to Discourse Data

Connecting to Discourse 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.

The Discourse API uses API Key authentication.

Using API Key Authentication

Discourse requires API Key and Username for authentication. API Keys are generated in the Discourse Admin panel under the API section. You can create user-specific API keys or all-users API keys. Once you have obtained the API Key, set it along with the Domain and Username in the ProfileSettings connection property.

Example Connection string

Profile=C:\profiles\Discourse.apip;ProfileSettings='Domain=forum.example.com;APIKey=your_api_key;Username=your_username;'AuthScheme=APIKey;

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

cnxn = mod.connect("Profile=C:\profiles\Discourse.apip;ProfileSettings='Domain=forum.example.com;APIKey=your_api_key;Username=your_username;'AuthScheme=APIKey;")

Create a SQL Statement to Query Discourse

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

sql = "SELECT ,  FROM Backups WHERE  = ''"

Extract, Transform, and Load the Discourse Data

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

Loading Discourse Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Discourse 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 Discourse 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\Discourse.apip;ProfileSettings='Domain=forum.example.com;APIKey=your_api_key;Username=your_username;'AuthScheme=APIKey;")

sql = "SELECT ,  FROM Backups WHERE  = ''"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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