Create Reports from Bitbucket Data in Looker Studio

Looker Studio, formerly known as Google Data Studio, empowers users to craft customized reports featuring data visualizations that can be shared with clients while reflecting your brand identity. When combined with CData Connect AI, you gain immediate cloud-to-cloud access to Bitbucket data to create visualizations, dashboards, and more. This article provides step-by-step instructions on establishing a virtual database for Bitbucket and generating reports from Bitbucket data within Looker Studio.

CData Connect AI offers a seamless cloud-to-cloud interface tailored for Bitbucket, making it straightforward to construct reports directly from live Bitbucket data within Looker Studio without the need for data replication. As you create visualizations, Looker Studio generates queries to retrieve data. With its inherent optimized data processing capabilities, CData Connect AI efficiently channels all supported query operations, including filters, JOINs, and more, directly to Bitbucket. This leverages server-side processing to swiftly provide the requested Bitbucket data.

Configure Bitbucket Connectivity for Looker Studio

Connectivity to Bitbucket from Looker Studio is made possible through CData Connect AI. To work with Bitbucket data from Looker Studio, we start by creating and configuring a Bitbucket connection.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "Bitbucket" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Bitbucket.

   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.
   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Bitbucket Connection page and update the User-based permissions.

With the connection configured, you are ready to connect to Bitbucket data from Looker Studio.

Visualize Live Bitbucket Data from Looker Studio

The steps below outline connecting to CData Connect AI from Looker Studio to create a new Bitbucket data source and build a simple visualization from the data.

1. Log into Looker Studio, click data sources, create a new data source, and choose CData Connect AI Connector.

2. Click Authorize and allow access to your Google account.
3. Click Authorize to authenticate with your CData Connect AI instance
4. In the CData Connect AI Connector in Looker Studio select Connections to import from the dropdown and click Next

5. Now select a Connection (e.g. Bitbucket1) and click Next
6. Select a Table (e.g. Issues) or use a Custom Query and click Connect to continue
7. If needed, modify columns, click Create Report, and add the data source to the report.

8. Select a visualization style and add it to the report.
9. Select Dimensions and Measures to customize your visualization.

Live Access to Bitbucket Data from Cloud Applications

Now you have a direct, cloud-to-cloud connection to live Bitbucket data from your Looker Studio workbook. You can create more data sources and new visualizations, build reports, and more — all without replicating Bitbucket data.

Try CData Connect AI and get real-time data access to hundreds of SaaS, Big Data, and NoSQL sources directly from your cloud applications.