Oracle Analytics Cloud

You are an Oracle Analytics Cloud developer. The user wants to build dashboards and data visualizations in Oracle Analytics Cloud (OAC).

What to check first

• Verify Oracle Analytics Cloud instance is provisioned and accessible via your tenancy's console URL (typically https://<instance-name>.analytics.ocp.oraclecloud.com)
• Confirm you have appropriate user role permissions (at minimum "BI Consumer" or "BI Author" role) in your OAC instance
• Check that your data source is connected — navigate to Data > Connections and verify the connection status (database, Autonomous Data Warehouse, or REST API)

Steps

1. Create a new project by clicking Create > Project in the home screen, then select your data source (dataset or table)
2. Drag dimension fields (like Region, Product) to the Rows area and measure fields (like Revenue, Quantity) to the Values area in the Visualizations panel
3. Change the default visualization type by clicking the Visualization button and selecting from bar charts, line charts, pie charts, scatter plots, or maps depending on your data dimensions
4. Add filters by dragging a dimension field to the Filters area, then configure the filter logic (equals, contains, date range) in the filter dialog
5. Create multiple visualizations on separate canvases by using the Add Canvas button, then arrange them side-by-side for dashboard layout
6. Apply sort order to visualizations by clicking Sort in the visualization menu and selecting ascending/descending on specific measures or dimensions
7. Create interactions between visualizations using Actions — select a visualization, click the actions menu, and link it to filter other canvases when clicked
8. Save the project as a dashboard by clicking Save > Save As and selecting Dashboard as the save type, then publish it for other users

Code

// Oracle Analytics Cloud REST API - Create and populate a dashboard programmatically
const oacInstanceUrl = 'https://<instance-name>.analytics.ocp.oraclecloud.com';
const authToken = '<your-bearer-token>'; // Obtain from OAC user settings

// Step 1: Create a new project
async function createDashboard(projectName, datasetId) {
  const projectPayload = {
    name: projectName,
    description: 'Dashboard created via API',
    type: 'project'
  };
  
  const createResponse = await fetch(`${oacInstanceUrl}/api/v2/projects`, {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${authToken}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify(projectPayload)
  });
  
  const project = await createResponse.json();
  const projectId = project.id;
  
  // Step 2: Add a canvas with visualizations
  const canvasPayload = {
    name: 'Main Dashboard',
    type

Note: this example was truncated in the source. See the GitHub repo for the latest full version.

Common Pitfalls

• Treating this skill as a one-shot solution — most workflows need iteration and verification
• Skipping the verification steps — you don't know it worked until you measure
• Applying this skill without understanding the underlying problem — read the related docs first

When NOT to Use This Skill

• When a simpler manual approach would take less than 10 minutes
• On critical production systems without testing in staging first
• When you don't have permission or authorization to make these changes

How to Verify It Worked

• Run the verification steps documented above
• Compare the output against your expected baseline
• Check logs for any warnings or errors — silent failures are the worst kind

Production Considerations

• Test in staging before deploying to production
• Have a rollback plan — every change should be reversible
• Monitor the affected systems for at least 24 hours after the change