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

# Overview

> Learn how to monitor the quality of your data tables with Openlayer

The quality of your data directly impacts the performance and trustworthiness of your AI systems and analytics.
But in production, datasets drift, pipelines break silently, and anomalies slip through unnoticed.

**Data quality monitoring** in Openlayer helps you continuously validate the **health of your tables** so you can detect issues before they cascade downstream.

## How it works

<Steps>
  <Step title="Connect a data source">
    Integrating with Openlayer begins by connecting your warehouse or lakehouse (e.g.,
    BigQuery, Databricks, Snowflake).

    See the [Connect a data source](/data-quality-monitoring/connect-data-source) guide for details.

    <img width="700" style={{ borderRadius: "0.5rem" }} src="https://mintcdn.com/openlayer-44/uwZVy8AI1ZEo3Xtn/images/documentation/connect_data_source.png?fit=max&auto=format&n=uwZVy8AI1ZEo3Xtn&q=85&s=8df291c528cbad215a1cae7d58b840a6" alt="Connect data source" data-path="images/documentation/connect_data_source.png" />
  </Step>

  <Step title="Select tables to monitor">
    After providing the necessary credentials, you can choose which tables you want to track.
    Openlayer automatically profiles them, capturing schema, distributions, and summary statistics.

    <img width="700" style={{ borderRadius: "0.5rem" }} src="https://mintcdn.com/openlayer-44/uwZVy8AI1ZEo3Xtn/images/documentation/column_distribution.png?fit=max&auto=format&n=uwZVy8AI1ZEo3Xtn&q=85&s=6a74df42e0ee343ad9cec15c5b3bf921" alt="Column distribution" data-path="images/documentation/column_distribution.png" />
  </Step>

  <Step title="Set up tests">
    Add tests on top of your tables.
    Common examples include schema checks (unexpected columns, type mismatches)
    and anomaly detection (sudden spikes or drops in key metrics, missing values, etc.)

    Tests can run automatically at regular cadence on top of your tables.

    <img width="700" style={{ borderRadius: "0.5rem" }} src="https://mintcdn.com/openlayer-44/uwZVy8AI1ZEo3Xtn/images/documentation/data_quality_test_results.png?fit=max&auto=format&n=uwZVy8AI1ZEo3Xtn&q=85&s=ad95bdabc0cf31688b6bddc44104fd97" alt="Data quality test results" data-path="images/documentation/data_quality_test_results.png" />
  </Step>

  <Step title="Get notified and act">
    Openlayer tracks test results over time and alerts you immediately when an anomaly is detected.
    This way, you can respond before bad data propagates into models, dashboards, or production systems.
  </Step>
</Steps>

## Next steps

By continuously monitoring table quality, Openlayer provides a feedback loop that keeps your data pipelines healthy and reliable.

To try it out, check out the [Connect a data source](/data-quality-monitoring/connect-data-source) guide.

## FAQ

<AccordionGroup>
  <Accordion title="Do I need to copy data into Openlayer?">
    No. Openlayer connects to your warehouse or lakehouse and runs tests directly on your tables.
    Data does not need to be replicated unless you explicitly choose to export results.
  </Accordion>

  <Accordion title="What data sources are supported?">
    Today, Openlayer supports BigQuery, Databricks, and Snowflake. We’re expanding
    coverage to additional warehouses and data lakes. See the [Integrations
    page](/integrations/overview) for the latest list.
  </Accordion>

  <Accordion title="How is this different from Observability?">
    * **Observability** focuses on tracing your AI system in production and testing its live requests.
    * **Data quality monitoring** focuses on the **tables feeding those systems**, helping you detect issues at the data source before they affect downstream models or apps.

    Many teams use both together: catch issues early in the data, and validate behavior in the AI system.
  </Accordion>
</AccordionGroup>