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Extract, Transform, and Load Confluence Data in Python

The CData Python Connector for Confluence enables you to create ETL applications and pipelines for Confluence data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Confluence and the petl framework, you can build Confluence-connected applications and pipelines for extracting, transforming, and loading Confluence data. This article shows how to connect to Confluence with the CData Python Connector and use petl and pandas to extract, transform, and load Confluence data.

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

Connecting to Confluence Data

Connecting to Confluence 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.

Obtaining an API Token

An API token is necessary for account authentication. To generate one, login to your Atlassian account and navigate to API tokens > Create API token. The generated token will be displayed.

Connect Using a Confluence Cloud Account

To connect to a Cloud account, provide the following (Note: Password has been deprecated for connecting to a Cloud Account and is now used only to connect to a Server Instance.):

  • User: The user which will be used to authenticate with the Confluence server.
  • APIToken: The API Token associated with the currently authenticated user.
  • Url: The URL associated with your JIRA endpoint. For example, https://yoursitename.atlassian.net.

Connect Using a Confluence Server Instance

To connect to a Server instance, provide the following:

  • User: The user which will be used to authenticate with the Confluence instance.
  • Password: The password which will be used to authenticate with the Confluence server.
  • Url: The URL associated with your JIRA endpoint. For example, https://yoursitename.atlassian.net.

After installing the CData Confluence Connector, follow the procedure below to install the other required modules and start accessing Confluence 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 Confluence 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.confluence as mod

You can now connect with a connection string. Use the connect function for the CData Confluence Connector to create a connection for working with Confluence data.

cnxn = mod.connect("User=admin;APIToken=myApiToken;Url=https://yoursitename.atlassian.net;Timezone=America/New_York;")

Create a SQL Statement to Query Confluence

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

sql = "SELECT Key, Name FROM Pages WHERE Id = '10000'"

Extract, Transform, and Load the Confluence Data

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

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for Confluence, you can work with Confluence 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 Confluence Python Connector to start building Python apps and scripts with connectivity to Confluence 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.confluence as mod

cnxn = mod.connect("User=admin;APIToken=myApiToken;Url=https://yoursitename.atlassian.net;Timezone=America/New_York;")

sql = "SELECT Key, Name FROM Pages WHERE Id = '10000'"

table1 = etl.fromdb(cnxn,sql)

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

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