Analyze Bitbucket Data in SAP Analytics Cloud

SAP Analytics Cloud is a cloud-based business intelligence platform. CData Connect AI creates a pure, cloud-to-cloud connection to Bitbucket and can be used to generate an OData API (natively supported in Analytics Cloud) for Bitbucket. By pairing SAP Analytics Cloud with CData Connect AI, you get true cloud-to-cloud connectivity to all of your SaaS and cloud-based Big Data and NoSQL sources — no need to migrate your data or write your integrations. Simply connect to Connect AI as you would any other OData service and get instant, consolidated access to all of your data.

In this article, we walk through connecting to Bitbucket from SAP Analytics Cloud (through CData Connect AI) to create a model and build a simple dashboard.

Connect to Bitbucket from SAP Analytics Cloud

To work with live Bitbucket data in SAP Analytics Cloud, we need to connect to Bitbucket from Connect AI, provide user access to the connection, and create a Workspace for the Bitbucket data.

Connect to Bitbucket from Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

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.

Configure Bitbucket Endpoints for SAP Analytics Cloud

After connecting to Bitbucket, create a workspace for your desired table(s).

1. Navigate to the Workspaces page and click Add to create a new Workspace (or select an existing workspace).
2. Click Add to add new assets to the Workspace.
3. Select the Bitbucket connection (e.g. Bitbucket1) and click Next.
4. Select the table(s) you wish to work with and click Confirm.
5. Make note of the OData Service URL for your workspace, e.g. https://cloud.cdata.com/api/odata/{workspace_name}

With the connection, PAT, and Workspace configured, you are ready to connect to Bitbucket data from SAP Analytics Cloud.

Create a Model of Bitbucket Data in SAP Analytics Cloud

With the connection to Bitbucket configured and the OData endpoint(s) created, we can create a Model for Bitbucket data in SAP Analytics Cloud.

1. Log into your Analytics Cloud instance and click Create -> Model from the menu.
2. Choose "Get data from a datasource" and select "OData Services"
3. Choose an existing connection to your Connect AI OData or Create a new one:
   • Set Connection Name
   • Set Data Service URL to the Base URL for your OData API: https://cloud.cdata.com/api/odata/{workspace_name}
   • Set Authentication Type to Basic Authentication
   • Set User Name to the Connect AI user (e.g. [email protected])
   • Set Password to the PAT for the above user
   
4. Choose "Create a new query" and click Next
5. Name the Execute, select an OData endpoint (like Issues) and click Next
6. Drag the columns you wish to work with into the Selected Data workspace and click Create
7. At this point, a Draft Data source is created; click the draft to finalize the model
8. Perform any transformations, including creating calculated dimensions, location dimensions, and combining data sources, then click Create Model
9. Name your model and click OK

Build a Dashboard in SAP Analytics Cloud

With the model created, you are ready to create a dashboard in SAP Analytics Cloud based on Bitbucket data.

1. From the menu, click Create -> Story
2. Click on SAP Analytics Template (this article uses the "Dashboard" template)
3. Choose a layout and click Apply
4. From the More menu, select a visualization to insert (Chart)
5. Select a model to visualize
6. Select a structure and the required Measures and Dimensions
7. Save the store

More Information & Free Trial

Now, you have created a simple but powerful dashboard from live Bitbucket data. For more information on creating OData feeds from Bitbucket (and more than 100 other data sources), visit the Connect AI page. Sign up for a free trial and start working with live Bitbucket data in SAP Analytics Cloud.