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

Getting Started

Install via Docker

docker run -p 3000:3000 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.

Features

• 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)

Limitations

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

Support

For general help using CueObserve, read the documentation, or go to Github Discussions.

To report a bug or request a feature, open an issue.

Contributing

We'd love contributions to CueObserve. Before you contribute, please first discuss the change you wish to make via an issue or a discussion. Contributors are expected to adhere to our code of conduct.