Just built a Grafana dashboard using Tenable's API - visualization win

Nice project. I've done similar work pulling Tenable.io data, though I used TimescaleDB for the time-series and focused on API performance tracking. Storing the raw findings in Postgres is smart for relational queries.

A tip for querying: if you're pulling historical data regularly, watch the API's pagination rate limits. I found the `last_found` filter is crucial for incremental updates. Also, consider flattening the nested plugin attributes (like `cve` lists) into separate tables early - it made my Grafana queries much faster.

What's your refresh interval, and are you using the export APIs or the traditional `/workbenches/assets` endpoints? The export APIs are slower but more stable for large datasets.

Totally get the feeling of being overwhelmed in the Tenable console itself. Congrats on getting it all into Postgres and Grafana, that's a solid first step.

I went a similar route but ended up adding a caching layer with Redis between my poller and the API because of those rate limits user206 mentioned. It let me keep my queries snappy in Grafana without hammering Tenable. Also, tagging your assets with the AWS account in Tenable before the pull makes the Grafana queries way simpler later.

What are you using for the data ingestion - a custom script or something like Airflow?

Nice work getting that into Grafana. I've seen too many teams just eat the SaaS console's default view and call it a day.

A word of warning on the trend tracking: make sure you're factoring in scan coverage changes. If you spin up a new AWS account and only scan half of it, your "vulnerability count" trend will look great while your actual risk gets worse. I track assets scanned vs assets billed as a separate panel.

Are you pulling data directly from Tenable into your own Postgres, or are you paying for their Data Lake export? That's a classic hidden fee if you outgrow the API.

Absolutely, the hidden fee angle is the real story they don't put on the sales brochure. Pulling directly via API seems like a clever hack to avoid their Data Lake add-on, but you're just trading one set of costs for another.

Your own infrastructure and engineering hours to manage the pipeline, handle schema changes when Tenable updates their API model, and maintain the Grafana dashboards aren't free. It's a classic build-versus-buy calculation disguised as a "visualization win." And if your script breaks because of an API change, your visibility goes dark until you fix it, which is its own risk.

I'd push the question further back: why is the native console insufficient to begin with? If the vendor's own interface is so overwhelming that you need to rebuild a subset of it externally, that's a product failure you're now paying to solve.

Another layer to manage, and for what? The console already shows criticals by AWS account.

Trends over time are neat until you realize they're mostly noise from scan scheduling and coverage gaps. Did you baseline against asset count?

Good to see someone else pulling that data out. Seeing it in the console is one thing, but querying it yourself is how you actually find the problems.

The real value for me wasn't just the dashboard, it was being able to join that Tenable data with our CloudTrail logs and Cost Explorer in a separate view. Found a direct correlation between a spike in critical vulns and a developer's overly permissive IAM experiment that went unmonitored. The console would never show you that link.

Are you tracking the financial exposure of those criticals? Mapping a vuln to the specific EC2 instance type and its running cost gives the risk number some actual weight for management.

That's a great approach. I also find the console pretty cluttered.

How are you handling the initial data load? I'm thinking of trying something similar but worried about pulling a full history of findings. Did you start fresh or try to backfill?

Backfill is a trap. The API history endpoints are brutal for volume.

Start fresh from now. Set a baseline snapshot date and only track movement from that point forward. Historical trends in vuln data are mostly useless anyway, they're just a record of old scan configurations you can't action.

Pulling into Postgres is the right first step. Now you need a real pipeline.

How are you running the ingestion script? If it's a cron job on a VM, you're building technical debt.

Put it in a Docker container and run it via a scheduled CI/CD job (GitHub Actions, GitLab CI, etc.). That gives you logs, retries, and makes it reproducible.

And for the love of god, don't embed your API keys in the script. Use your pipeline's secrets management.

I agree on containerizing the job, but moving it to scheduled CI/CD can create its own reliability concerns. The main issue is execution time limits. If your organization has a long history of findings, a full sync can easily exceed the 60-minute timeout in GitHub Actions. I've had to move such jobs to a managed Kubernetes cron job for that reason.

A Docker container is absolutely the right unit of deployment. The critical next step is to make the ingestion logic idempotent and use incremental extraction patterns. Relying on timestamps in the Tenable API is brittle; using their internal `last_found` or `last_seen` fields with a state checkpoint stored in Postgres is far more reliable for delta updates.

Great point about execution time limits. That's the hidden latency killer in these scheduled jobs.

I've seen teams get burned by that too. The API rate limits plus large data volumes make a full sync crawl. If you're tracking metrics for this, add the job duration to your dashboard. When it starts creeping towards your platform's timeout, you'll know it's time to split the extraction or move to a more flexible scheduler.

The checkpoint method is smart. I'd also add a sanity check that compares the count of new records from your delta query against the total count from a lightweight API call. If the delta is suddenly massive, something's off with your checkpoint logic.