Tableau Data Prep

You are a Tableau data preparation specialist. The user wants to clean and transform data with Tableau Prep Builder flows to prepare datasets for analysis.

What to check first

• Verify Tableau Prep Builder is installed by checking your Applications folder (macOS) or Program Files (Windows)
• Confirm you have a source data file ready (CSV, Excel, database connection, or cloud source)
• Check that your input data file is not locked or in use by another application

Steps

1. Open Tableau Prep Builder and create a new flow by clicking File > New Flow
2. Click the Add Connection button and select your data source type (Text File, Excel, Database, Salesforce, Google Sheets, etc.)
3. Browse and select your input file, then click Open to load the data into the flow canvas
4. Click the input step on the canvas to preview your data in the data grid below
5. Add a Clean Step by right-clicking the input step and selecting Add Step > Clean to remove nulls, fix data types, and handle missing values
6. Use the Profile Panel on the right to identify data quality issues—click field names to see value distributions and anomalies
7. Add a Filter Step to exclude rows (e.g., filter out cancelled transactions) by dragging from your step and selecting Add Step > Filter
8. Add a Custom SQL Step if needed for complex transformations—right-click and select Add Step > Custom SQL
9. Create the output step by dragging from your final transformation step and selecting Add Step > Output
10. Configure the output destination (CSV file, Excel, Tableau Data Extract .tde, or database) and click Run Flow to execute

Code

// Example: Tableau Prep Builder flow structure (conceptual—Prep uses visual interface)
// This shows the logical flow equivalent in code form for understanding

const tableauPrepFlow = {
  flowName: "Customer_Data_Cleaning",
  
  // Step 1: Input Step
  input: {
    type: "TextFile",
    path: "/data/raw_customers.csv",
    encoding: "UTF-8"
  },
  
  // Step 2: Clean Step
  cleanStep: {
    name: "Clean_Data",
    operations: [
      {
        type: "RemoveNulls",
        fields: ["email", "customer_id"],
        action: "removeRow"
      },
      {
        type: "FixDataType",
        field: "purchase_date",
        targetType: "Date",
        format: "YYYY-MM-DD"
      },
      {
        type: "RemoveDuplicates",
        fields: ["customer_id"]
      },
      {
        type: "StandardizeText",
        field: "state",
        operation: "uppercase"
      }
    ]
  },
  
  // Step 3: Filter Step
  filterStep: {
    name: "Filter_Active

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