{"id":13720721,"url":"https://github.com/cuebook/CueObserve","last_synced_at":"2025-05-07T12:33:11.062Z","repository":{"id":37777451,"uuid":"379364983","full_name":"cuebook/CueObserve","owner":"cuebook","description":"Timeseries Anomaly detection and Root Cause Analysis on data in SQL data warehouses and databases","archived":false,"fork":false,"pushed_at":"2022-02-23T19:51:23.000Z","size":6446,"stargazers_count":229,"open_issues_count":30,"forks_count":24,"subscribers_count":15,"default_branch":"latest_release","last_synced_at":"2025-04-10T00:24:24.825Z","etag":null,"topics":["anomaly","anomaly-detection","bigquery","datawarehouse","prophet-facebook","redshift","root-cause-analysis","snowflake","sql","timeseries-analysis","timeseries-forecasting"],"latest_commit_sha":null,"homepage":"https://cueobserve.cuebook.ai","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cuebook.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-06-22T18:26:27.000Z","updated_at":"2025-04-09T07:32:09.000Z","dependencies_parsed_at":"2022-08-18T17:52:31.517Z","dependency_job_id":null,"html_url":"https://github.com/cuebook/CueObserve","commit_stats":null,"previous_names":[],"tags_count":6,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuebook%2FCueObserve","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuebook%2FCueObserve/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuebook%2FCueObserve/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuebook%2FCueObserve/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cuebook","download_url":"https://codeload.github.com/cuebook/CueObserve/tar.gz/refs/heads/latest_release","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252876699,"owners_count":21818229,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["anomaly","anomaly-detection","bigquery","datawarehouse","prophet-facebook","redshift","root-cause-analysis","snowflake","sql","timeseries-analysis","timeseries-forecasting"],"created_at":"2024-08-03T01:01:07.324Z","updated_at":"2025-05-07T12:33:06.050Z","avatar_url":"https://github.com/cuebook.png","language":"Python","readme":"# Overview\n\n[![CueObserve Logo](.gitbook/assets/cueObserve.png)](https://cueobserve.cuebook.ai)\n\n[![](https://api.codeclimate.com/v1/badges/a70e071b59d5dbc38846/maintainability)](https://codeclimate.com/github/cuebook/CueObserve/maintainability) [![](https://api.codeclimate.com/v1/badges/a70e071b59d5dbc38846/test\\_coverage)](https://codeclimate.com/github/cuebook/CueObserve/test\\_coverage) [![Test Coverage](https://github.com/cuebook/cueobserve/actions/workflows/pr\\_checks.yml/badge.svg) ](https://github.com/cuebook/cueobserve/actions/workflows/pr\\_checks.yml)[![License](https://img.shields.io/github/license/cuebook/cueobserve)](https://github.com/cuebook/cueobserve/blob/main/LICENSE.md)\n\n\n\nCueObserve helps you monitor your metrics. Know when, where, and why a metric isn't right.\n\nCueObserve 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.\n\nCueObserve works with data in your SQL data warehouses and databases. It currently supports Snowflake, BigQuery, Redshift, Druid, Postgres, MySQL, SQL Server and ClickHouse.\n\n![CueObserve Anomaly](\u003c.gitbook/assets/Overview\\_Anomaly (1).png\u003e) ![CueObserve RCA](\u003c.gitbook/assets/Overview\\_RCA (1).png\u003e)\n\n### Getting Started\n\nInstall via docker-compose\n\n```\nmkdir -p ~/cuebook\nwget https://raw.githubusercontent.com/cuebook/CueObserve/latest_release/docker-compose-prod.yml -q -O ~/cuebook/docker-compose-prod.yml\nwget https://raw.githubusercontent.com/cuebook/CueObserve/latest_release/.env -q -O ~/cuebook/.env\ncd ~/cuebook\n```\n\n```\ndocker-compose -f docker-compose-prod.yml --env-file .env up -d\n```\n\nNow visit [localhost:3000](http://localhost:3000) in your browser.\u0026#x20;\n\n### Demo Video\n\n[![Watch CueObserve video](http://img.youtube.com/vi/VZvgNa65GQU/hqdefault.jpg)](http://www.youtube.com/watch?feature=player\\_embedded\\\u0026v=VZvgNa65GQU)\n\n### How it works\n\nYou write a SQL GROUP BY query, map its columns as dimensions and measures, and save it as a virtual Dataset.\n\n![Dataset SQL](\u003c.gitbook/assets/Dataset\\_SQL\\_cropped (1).png\u003e)\n\n![Dataset Schema Map](\u003c.gitbook/assets/Dataset\\_Mapping\\_cropped (1).png\u003e)\n\nYou then define one or more anomaly detection jobs on the dataset.\n\n![Anomaly Definition](\u003c.gitbook/assets/AnomalyDefinitions (1).png\u003e)\n\nWhen an anomaly detection job runs, CueObserve does the following:\n\n1. Executes the SQL GROUP BY query on your data warehouse and stores the result as a Pandas dataframe.\n2. Generates one or more timeseries from the dataframe, as defined in your anomaly detection job.\n3. Generates a forecast for each timeseries using [Prophet](https://github.com/facebook/prophet).\n4. Creates a visual card for each timeseries. Marks the card as an anomaly if the last data point is anomalous.\n\n### Features\n\n* Automated SQL to timeseries transformation.\n* Run anomaly detection on the aggregate metric or split it by any dimension. Limit the split to significant dimension values.\n* Use Prophet or simple mathematical rules to detect anomalies.\n* In-built Scheduler. CueObserve uses Celery as the executor and celery-beat as the scheduler.\n* Slack alerts when anomalies are detected.\n* Monitoring. Slack alert when a job fails. CueObserve maintains detailed logs.\n\n#### Limitations\n\n* Currently supports Prophet for timeseries forecasting.\n* Not being built for real-time anomaly detection on streaming data.\n\n### Support\n\nFor general help using CueObserve, read the [documentation](https://cueobserve.cuebook.ai), or go to [Github Discussions](https://github.com/cuebook/cueobserve/discussions).\n\nTo report a bug or request a feature, open an [issue](https://github.com/cuebook/cueobserve/issues).\n\n### Contributing\n\nWe'd love contributions to CueObserve. Before you contribute, please first discuss the change you wish to make via an [issue](https://github.com/cuebook/cueobserve/issues) or a [discussion](https://github.com/cuebook/cueobserve/discussions). Contributors are expected to adhere to our [code of conduct](https://github.com/cuebook/cueobserve/blob/main/CODE\\_OF\\_CONDUCT.md).\n","funding_links":[],"categories":["Anomaly Detection Software"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcuebook%2FCueObserve","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcuebook%2FCueObserve","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcuebook%2FCueObserve/lists"}