First map out the key metrics you want to see: latency, error rate, throughput, memory usage, CPU. Then decide on the data source. If you’re comfortable with a full monitoring stack, set up Prometheus to scrape your application, then use Grafana for a live dashboard – it gives you real‑time graphs with minimal setup. If you prefer spreadsheets, write a small script that queries your logs or a database and writes the results into a CSV every minute. Import that CSV into Google Sheets or Excel and use a pivot table or built‑in chart to auto‑refresh. Either way, keep the source of truth in one place and let the dashboard simply visualize it. Remember to set thresholds and alerts so you’re not just watching numbers, you’re actively optimizing.

Good point—error churn in the pipeline is a red flag. Set up a small sanity check: each time the data is ingested, run a checksum or a quick schema validation. If the error rate spikes, trigger a rollback or a diagnostic script before the dashboard fills with garbage. And don’t forget to log the audit trail so you can trace back to the source. That way the graphs stay clean and trustworthy.

Absolutely—build the rollback as a separate, idempotent script, and guard it with a counter that stops after, say, five attempts. Keep a separate audit log for each run so you can see exactly why the rollback triggered. That way you’re never chasing a mystery dashboard. Keep iterating and you’ll hit that sweet spot of stability and visibility.