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

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

Connecting to Bitbucket Data

Connecting to Bitbucket 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.

For most queries, you must set the Workspace. The only exception to this is the Workspaces table, which does not require this property to be set, as querying it provides a list of workspace slugs that can be used to set Workspace. To query this table, you must set Schema to 'Information' and execute the query SELECT * FROM Workspaces>.

Setting Schema to 'Information' displays general information. To connect to Bitbucket, set these parameters:

• Schema: To show general information about a workspace, such as its users, repositories, and projects, set this to Information. Otherwise, set this to the schema of the repository or project you are querying. To get a full set of available schemas, query the sys_schemas table.
• Workspace: Required if you are not querying the Workspaces table. This property is not required for querying the Workspaces table, as that query only returns a list of workspace slugs that can be used to set Workspace.

Authenticating to Bitbucket

Bitbucket supports OAuth authentication only. To enable this authentication from all OAuth flows, you must create a custom OAuth application, and set AuthScheme to OAuth.

Be sure to review the Help documentation for the required connection properties for you specific authentication needs (desktop applications, web applications, and headless machines).

Creating a custom OAuth application

From your Bitbucket account:

1. Go to Settings (the gear icon) and select Workspace Settings.
2. In the Apps and Features section, select OAuth Consumers.
3. Click Add Consumer.
4. Enter a name and description for your custom application.
5. Set the callback URL:
   • For desktop applications and headless machines, use http://localhost:33333 or another port number of your choice. The URI you set here becomes the CallbackURL property.
   • For web applications, set the callback URL to a trusted redirect URL. This URL is the web location the user returns to with the token that verifies that your application has been granted access.
6. If you plan to use client credentials to authenticate, you must select This is a private consumer. In the driver, you must set AuthScheme to client.
7. Select which permissions to give your OAuth application. These determine what data you can read and write with it.
8. To save the new custom application, click Save.
9. After the application has been saved, you can select it to view its settings. The application's Key and Secret are displayed. Record these for future use. You will use the Key to set the OAuthClientId and the Secret to set the OAuthClientSecret.

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

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

cnxn = mod.connect("Workspace=myworkspaceslug;Schema=Information;InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Bitbucket

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

sql = "SELECT Title, ContentRaw FROM Issues WHERE Id = '1'"

Extract, Transform, and Load the Bitbucket Data

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

Loading Bitbucket Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the Issues table.

Adding New Rows to Bitbucket

table1 = [ ['Title','ContentRaw'], ['NewTitle1','NewContentRaw1'], ['NewTitle2','NewContentRaw2'], ['NewTitle3','NewContentRaw3'] ]

etl.appenddb(table1, cnxn, 'Issues')

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

cnxn = mod.connect("Workspace=myworkspaceslug;Schema=Information;InitiateOAuth=GETANDREFRESH;")

sql = "SELECT Title, ContentRaw FROM Issues WHERE Id = '1'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Title','ContentRaw'], ['NewTitle1','NewContentRaw1'], ['NewTitle2','NewContentRaw2'], ['NewTitle3','NewContentRaw3'] ]

etl.appenddb(table3, cnxn, 'Issues')