docs/key-features.mdx

Rama master · réplica de lectura del repositorio original.

---
title: "Elementary Key Features"
sidebarTitle: "Key Features"
icon: "stars"
---

<CardGroup cols={1}>
  <Card
    title="Data anomaly detection"
    icon="monitor-waveform"
    iconType="solid"
    href="/features/data-tests"
  >
    Monitor data quality metrics, freshness, volume and schema changes.
  </Card>
  <Card
    title="dbt artifacts and run results"
    icon="table-tree"
    iconType="regular"
    href="/dbt/dbt-artifacts"
  >
    Upload metadata, run and test results to tables as part of your jobs.
  </Card>
  <Card
    title="Alerts"
    icon="bell-exclamation"
    iconType="regular"
    href="/features/elementary-alerts"
  >
    Send informative alerts to different channels and users.
  </Card>
  <Card
    title="Data observability dashboard"
    icon="browsers"
    iconType="solid"
    href="/features/data-observability-dashboard"
  >
    Inspect your data health overview, test results, models performance and data
    lineage.
  </Card>
  <Card
    title="End-to-end data lineage"
    icon="arrow-progress"
    iconType="solid"
    href="/features/lineage"
  >
    Inspect dependencies including Column Level Lineage and integration with BI
    tools.
  </Card>
  <Card
    title="Automated monitors"
    href="/features/automated-monitors"
    icon="wand-magic-sparkles"
    iconType="solid"
  >
    Out of the box freshness, volume and schema monitoring.
  </Card>
  <Card
    title="Configuration as code"
    href="/features/config-as-code"
    icon="code"
    iconType="solid"
  >
    All the Elementary configuration is managed in your dbt code.
  </Card>
  <Card
    title="Data Catalog"
    icon="folder-tree"
    iconType="solid"
    href="/features/catalog"
  >
    Explore and discover data sets, manage your documentation in code.
  </Card>
</CardGroup>



#### Anomaly Detection

<Card title="Automated Volume & Freshness Monitors">
  Out-of-the-box ML-powered monitoring for freshness and volume issues on all production tables.
  The monitors track updates to tables, and will detect data delays, incomplete updates, and significant volume changes.   
  By qurying only metadata (e.g. information schema, query history), the monitors don't add compute costs.
</Card>

<Card title="Opt-in Anomaly Detection Monitors">
  ML-powered anomaly detection on data quality metrics such as null rate, empty values, string length, numeric metrics (sum, max, min, avg), etc.
  Elementary also supports monitoring for anomalies by dimensions.
  The monitors are activated for specific data sets, and require minimal configuration (e.g. timestamp column, dimensions). 
</Card>

#### Schema Validation

<Card title="Schema Tests">
  Elementary offers a set of schema tests for validating there are no breaking changes. 
  The tests support detecting any schema changes, only detecting changes from a configured baseline, JSON schema validation, 
  and schema changes that break downstream exposures such as dashboards.
</Card>

<Card title="Automated Schema Monitors">
  Coming soon!
</Card>

#### Data Tests

Custom SQL Tests

dbt tests

Python tests

#### Tests Coverage

#### Performance monitoring