> ## Documentation Index
> Fetch the complete documentation index at: https://docs.corelayer.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Anomaly Detection

> Monitor your data pipelines and infrastructure for unexpected changes in volume, column values, and schema.

## What is Anomaly Detection?

Corelayer continuously monitors your data for anomalies using statistical baselines. When data deviates from expected patterns, Corelayer flags it as an anomaly with a severity level and provides context to help you debug the issue.

There are two ways to set up anomaly detection:

* **Table Monitoring** — Connect a database integration and monitor tables directly. Corelayer tracks row volume, column values, and schema changes automatically.
* **SDK Metrics** — Instrument your code with the Corelayer SDK to track custom metrics from any data pipeline, ETL job, or application.

## How It Works

### 1. Configure a Rule

Select a database table or codebase and configure what to monitor. Choose between volume rules, column rules, and schema detection.

### 2. Baseline Collection

Corelayer collects data to build a statistical baseline for each partition. During this phase, the rule shows a **Collecting** status. No anomalies are reported until the baseline is ready.

### 3. Anomaly Detection

Once the baseline is ready, Corelayer compares new data against expected ranges. Values outside the threshold (calculated using mean, standard deviation, and a k-sigma multiplier) are flagged as anomalies.

### 4. Review Findings

Anomalies appear on the detail page with severity levels (**Critical**, **High**, **Medium**, **Low**, **Info**). Each finding shows the observed value, expected value, and the threshold bounds.

## Key Concepts

* **Partition** — A logical grouping of data. For time-series data, this is a time bucket. For categorical data, this is a unique combination of partition column values.
* **Volume Rule** — Monitors the row count per partition. Detects unexpected spikes or drops in data volume.
* **Column Rule** — Monitors the values of specific numerical columns. Detects shifts in column distributions.
* **Schema Rule** — Detects unexpected changes to table schema (added, removed, or modified columns).
* **Baseline** — The statistical model (mean, standard deviation, data points) built from historical data. Used to calculate expected ranges.
* **Frequency** — How often data is expected to arrive (hourly, daily, weekly, monthly). Helps Corelayer calibrate detection sensitivity.

## Getting Started

<CardGroup cols={2}>
  <Card title="Table Monitoring" icon="database" href="/anomalies/table-monitoring">
    Monitor database tables for volume, column, and schema anomalies.
  </Card>

  <Card title="SDK Metrics" icon="code" href="/anomalies/sdk-metrics">
    Track custom metrics from your data pipelines using the Corelayer SDK.
  </Card>
</CardGroup>

Need help? [Contact support](mailto:support@corelayer.com) for assistance with anomaly detection.