With CueObserve, you can run anomaly detection on data in your SQL data warehouses and databases.


Getting Started

Install via Docker

docker run -p 3000:80 cuebook/cueobserve

Now visit http://localhost:3000 in your browser.

How it works

You write a SQL GROUP BY query, map its columns as dimensions and measures, and save it as a virtual Dataset.



You then define one or more anomaly detection jobs on the dataset.


When an anomaly detection job runs, CueObserve does the following:

  1. Executes the SQL GROUP BY query on your data warehouse and stores the result as a Pandas dataframe.
  2. Generates one or more timeseries from the dataframe, as defined in your anomaly detection job.
  3. Generates a forecast for each timeseries using Prophet.
  4. Creates a visual card for each timeseries. Marks the card as an anomaly if the last data point is anomalous.


  • Automated SQL to timeseries transformation.
  • Run anomaly detection on the aggregate metric or break it down by any dimension.
  • In-built Scheduler. CueObserve uses Celery as the executor and celery-beat as the scheduler.
  • Slack alerts when anomalies are detected. (coming soon)
  • Monitoring. Slack alert when a job fails. CueObserve maintains detailed logs. (coming soon)


  • Currently supports Prophet for timeseries forecasting.
  • Not being built for real-time anomaly detection on streaming data.