CueObserve helps you monitor your metrics. Know when, where, and why a metric isn't right.
CueObserve uses timeseries Anomaly detection to find where and when a metric isn't right. It then offers one-click Root Cause analysis so that you know why a metric isn't right.
CueObserve works with data in your SQL data warehouses and databases. It currently supports Snowflake, BigQuery, Redshift, Druid, Postgres, MySQL, SQL Server and ClickHouse.
Install via Docker
wget https://raw.githubusercontent.com/cuebook/CueObserve/latest_release/docker-compose.yml -q -O cueobserve-docker-compose.ymldocker-compose -f cueobserve-docker-compose.yml up -d
Now visit http://localhost:3000 in your browser.
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:
For general help using CueObserve, read the documentation, or go to Github Discussions.
To report a bug or request a feature, open an issue.
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.
Source code: https://github.com/cuebook/CueObserve
License: Apache-2.0 License