🔍 What You're Doing
This is the final shaping step. The Augment Events Pipeline produced an enriched dataset with all the joined context, but not yet in a clean, analyst-ready schema. This Output Pipeline takes that enriched output and reshapes it to match a target schema you define using a sample CSV file (AUDIT_LOG.csv).
You upload the sample to AI Copilot as a target, paste a short prompt describing the cleanup operations you want, and AI Copilot generates the column renames, reordering, and date formatting needed to match. The result is a stable, predictable audit log dataset ready for downstream consumption such as BI tools, scheduled exports, or alerting workflows.
This pattern (sample-driven output shaping) is the fastest way to lock a Pipeline to a specific schema without writing transformation logic by hand.
📥 Before You Begin
Download AUDIT_LOG.csv to your local Downloads folder. You'll upload it as the sample target output for AI Copilot in the steps below.
✅ Summary of Steps
From the Workflow's Pipelines tab, click Add to create a new Output Pipeline.
Enter Create Final Output in the Name field and click AI Copilot.
Click Add Datasets and select the Augment Events Supporting Pipeline output.
Click into the description text area and paste the prompt below. Drag the resize handle to expand if needed.
Click Upload Sample Output Data, then Choose a CSV, and select AUDIT_LOG.csv from your Downloads folder.
Click Upload & Preview CSV.
Click Generate Instructions, review the AI-generated Pipeline Build Plan, and click Approve & Build.
Watch the Pipeline build each transformation, then click the last step to review the final output.
📋 Prompt to Copy
Paste this into the AI Copilot description field in step 4:
Rename the remaining mixed-case columns in the source based on naming conventions in the Sample Output Data. There are more columns in the Sample Output Data than in the source data. Do not raise an error or warning on this.
Then, reorder the columns in the source based on the approximate order in the Sample Output Data. There are more columns in the Sample Output Data than in the source data. Do not raise an error or warning on this.
Then, convert EVENT_DATE to a format that looks like this "2023-06-22 20:03:08"
