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Showing content from https://github.com/cuebook/CueObserve below:

cuebook/CueObserve: Timeseries Anomaly detection and Root Cause Analysis on data in SQL data warehouses and databases

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-compose

mkdir -p ~/cuebook
wget https://raw.githubusercontent.com/cuebook/CueObserve/latest_release/docker-compose-prod.yml -q -O ~/cuebook/docker-compose-prod.yml
wget https://raw.githubusercontent.com/cuebook/CueObserve/latest_release/.env -q -O ~/cuebook/.env
cd ~/cuebook

docker-compose -f docker-compose-prod.yml --env-file .env up -d

Now visit 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:

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 split it by any dimension. Limit the split to significant dimension values.
• Use Prophet or simple mathematical rules to detect anomalies.
• In-built Scheduler. CueObserve uses Celery as the executor and celery-beat as the scheduler.
• Slack alerts when anomalies are detected.
• Monitoring. Slack alert when a job fails. CueObserve maintains detailed logs.

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

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.

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