Start with clean, trustworthy data

Data quality monitoring

Start with clean, trustworthy data

Monitor your pipelines for anomalies and schema drift to prevent downstream model failures.

Measure data quality

Set up automated tests for null values, outliers, schema changes, missing features, and more.

Automatically detect anomalous column values

Identify unusual spikes, dips, or patterns using adaptive thresholds and seasonality-aware learning.

Connect to your data sources

Run checks at the source—integrating directly with your data warehouse or lake, including Snowflake, BigQuery, Databricks, and Redshift.

Why it matters

Bad data leads to bad decisions

Poor data quality is one of the most common reasons ML models fail. It silently erodes model performance, triggers false alarms, and undermines business confidence. Openlayer helps you find and fix data issues before they impact downstream systems.

Use cases

Monitor data pipelines in real time

Whether you’re working with tabular datasets, logs, event streams, or multimodal data, Openlayer ensures your pipelines remain clean, stable, and trustworthy—from ingestion to inference.

Why Openlayer

From reactive firefighting to proactive data validation

Automated anomaly detection and schema validationSpot outliers, missing fields, and format drift the moment they land—before they break downstream jobs.

Runs as part of your data pipeline or workflowDrop checkpoints directly into your pipelines so every batch and stream is validated on the fly.

Easily integrates with data lakes and warehousesPlug directly into BigQuery, Databricks, Snowflake, and many other platforms with zero-copy connectors—no data movement required.

Keeps data clean at scale without manual effortLet Openlayer quarantine bad records, trigger alerts, and auto-generate issue tickets so your team can focus on building, not patching.

Integrations

Works with the tools you already use

Supports connections to cloud storage, databases, Airflow, dbt, and more. Trigger checks via CLI or API. No need to move your data.

Customers

Trustworthy data from day one

“Before Openlayer, we were always one step behind our data issues. Now, we catch them before they cause problems.”

Chief Data Officer and Analytics at Retail Company

FAQs

Your questions, answered

What is data quality monitoring?

How does Openlayer detect anomalies?

Do I need to move my data into Openlayer?

Can I define custom rules or thresholds?

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Data quality monitoring