This Gist outlines a basic extension of the Raspi-Sump project. It takes the data/output from the project's logfile and feeds to the Telegraf agent (locally on the Pi), which in turn feeds it to an InfluxDB time-series database, which is then graphed using Grafana. All of these tools are freely available and setup is beyond this scope.
I wanted to quickly capture what I did for future reference, and to benefit others who may have been equally inspired by Al's project.
I've enhanced the bash script to compare the previous/current water levels in an effort to detect run cycles. If the water level has dropped by at least 3 inches (since the last run) we write an additonal entry to Telegraf, which gives us the ability to put some fancier data into Grafana. Yes, I'm making some presumptions here, but running data collection every 1m makes it a fairly safe bet. We don't know (definitively) the pump ran, we're just presuming it did because the water level changed (significantly).
I have a Pi 2b, its several years old. It's idle 100% of the time - running psump.py every 1m is practically immeasurable. Telegraf->InfluxDB->Grafana is implemented as a distributed model. Only the Telegraf agent runs on the Pi (with the raspi-sump scripts). Telegraf looks for changes in a particular places (one being my logfile), I believe every 15ms (as part of a larger system/resources scan), and pushes said changes to InfluxDB across the network. I run InfluxDB in a Docker container on my main computer, and Grafana similarly in it's own Docker container. More on setting those up here and here.
The time-series trio and architecture is quite lean and powerful. You should read up on them and all the diverse hooks they offer - monitor anything from AWS Cloudformations to database queries to disk I/O. Using it for raspi-sump was me applying work experience to home-hobbies.
There are larger, more complex monitoring features I want to incorporate in phase 2; Grafana & InfluxDB will play bigger roles then. Grafana also has built-in alerting abilities; see that red line in my sample graph! Eventually I want to stream the data out of InfluxDB and do some machine learning on it (cycle times, predictive failures, possible clogs, etc.)...but for now at least I'm capturing the data.