Analyze Bitbucket Data in Looker

Looker is a business intelligence and big data analytics platform that helps you explore, analyze and share real-time business analytics. When paired with CData Connect AI, you get instant, cloud-to-cloud access to Bitbucket data for business applications. This article shows how to connect to Bitbucket in Connect AI and then connect to Bitbucket data in Looker.

CData Connect AI provides a pure cloud-to-cloud interface for Bitbucket, allowing you to build reports from live Bitbucket data in Looker — without replicating the data to a natively supported database. As you create applications to work with data, Looker generates SQL queries to gather data. Using optimized data processing out of the box, CData Connect AI pushes all supported SQL operations (filters, JOINs, etc.) directly to Bitbucket, leveraging server-side processing to quickly return the requested Bitbucket data.

Configure Bitbucket Connectivity for Looker

Connectivity to Bitbucket from Looker is made possible through CData Connect AI. To work with Bitbucket data from Looker, 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.

Add a Personal Access Token

When connecting to Connect AI through the REST API, the OData API, or the Virtual SQL Server, a Personal Access Token (PAT) is used to authenticate the connection to Connect AI. It is best practice to create a separate PAT for each service to maintain granularity of access.

1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, you are ready to connect to Bitbucket data from Looker.

Connect to Bitbucket in Looker

The steps below outline connecting to CData Connect AI from Looker to create a new Bitbucket data source.

1. Log-in to Looker
2. In the navigation pane, select Admin.
3. Under the Database category, select Connections.
4. On the Connections page, click Add Connection.
5. Enter the connection settings:
   • Name: the name for the connection in models.
   • Dialect: select Microsoft SQL Server 2017+.
   • SSH Server: leave this disabled.
   • Remote Host:Port: enter tds.cdata.com in the first field and 14333 in the second field.
   • Database: enter the Connection Name of the CData Connect AI data source you want to connect to (for example, QuickBooksOnline1).
   • Username: enter your CData Connect AI username. This is displayed in the top-right corner of the CData Connect AI interface. For example, [email protected]
   • Password: enter the PAT you generated on the Settings page
   Leave the rest of the connection settings at their default values unless you need to modify them.
6. At the bottom of the page, click Test These Settings to ensure that you can connect to CData Connect AI.
7. Click Add Connection to create the connection and return to the Connections page.

Your connection is now available for use in Looker. To connect to additional data sources from your CData Connect AI account, repeat the setup steps above, changing the value for Database for each data source.

Creating A Looker Visualization From The SQL Runner and Explore Features

To create a visualization in Looker using the SQL Runner, follow these steps:

1. In the Looker interface, select Develop -> SQL Runner from the left navigation pane.
2. On the SQL Runner interface, select the connection you made in the previous steps.
3. Now, click the gear symbol next to a table and then select Explore Table.
4. Next, on the left menu, select fields from the table and click Run. You can now expand the Visualization accordion, and see a bar chart, by default.

   We have now created a visualization of Bitbucket data in Looker using CData Connect AI!

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