Data observability done right

Monitor data pipelines in minutes.

Scale your data platform reliably and provide trusted data products.

Works in your dbt™ code and integrates with your stack.

Automatically measure data health by domain

A data quality dashboard from Elementary showing key metrics like total health score (97%), total tests (10), and health score trends over time. It also displays quality dimension scores for completeness, uniqueness, freshness, validity, accuracy, and consistency, with visualizations for each metric.

Instant visibility into quality and performance

A composite view of dashboards from Elementary highlighting key data observability metrics. The dashboards include visualizations for test results, table health, monitored tables, schema changes, anomalies, and model runs, with donut charts, line graphs, and detailed metric breakdowns.

Automatic column-level lineage

A visual representation of a data model lineage in Elementary, showing relationships between models and their dependencies. Nodes like 'stg_customers,' 'stg_orders,' and 'customers' are connected, with annotations indicating column counts and statuses.

Automatically group tests into incidents

A dashboard displaying a list of incidents, including filters for 'unassigned' and 'high priority,' with columns for the incident name, date, and other details. The interface shows 24 open incidents with timestamps and metadata.

Track your model performance over time

Composite view of two dashboards from Elementary: one showing data tests with descriptions, results, and configurations alongside a line chart; the other displaying model performance metrics, including execution times, errors, and historical trends for multiple models.

Automatically add anomaly monitors to your data

A detailed monitoring dashboard from Elementary showcasing data tests for a dataset. It includes a graph tracking total row count over time, alongside a table displaying descriptions, results, configurations, and timestamps for each test. Filters and settings for dimensions and metrics are visible at the top.

Overview

Instant visibility into quality and performance

A composite view of dashboards from Elementary highlighting key data observability metrics. The dashboards include visualizations for test results, table health, monitored tables, schema changes, anomalies, and model runs, with donut charts, line graphs, and detailed metric breakdowns.

Lineage

Automatic column-level lineage

A visual data lineage diagram showing the relationships between models like 'stg_customers,' 'stg_orders,' 'customers,' 'customer_conversions,' and 'Users,' with metrics for columns and test results displayed on each model.

Incidents

Automatically group tests into incidents

A dashboard displaying a list of 24 open incidents with filters for unassigned and high priority, showing incident types, associated tests, timestamps, and status icons.

Performance

Track your model performance over time

Composite view of two dashboards from Elementary: one showing data tests with descriptions, results, and configurations alongside a line chart; the other displaying model performance metrics, including execution times, errors, and historical trends for multiple models.

Detection

Automatically add anomaly monitors to your data

A detailed monitoring dashboard from Elementary showcasing data tests for a dataset. It includes a graph tracking total row count over time, alongside a table displaying descriptions, results, configurations, and timestamps for each test. Filters and settings for dimensions and metrics are visible at the top.

Data Health

Automatically measure data health by domain

A data quality dashboard from Elementary showing key metrics like total health score (97%), total tests (10), and health score trends over time. It also displays quality dimension scores for completeness, uniqueness, freshness, validity, accuracy, and consistency, with visualizations for each metric.

Open quote mark

If you already invested in dbt and did the hard work of transforming how your organization thinks about data—

Elementary is a no-brainer.

Jing Wang, Data Engineering Manager, Thrive Market

Part of your dbt workflow

Ingests existing dbt tests, tags and configurations

Software engineering best practices makes it easy to scale and maintain

A tool that meets you where you are,  and takes you where you want to go

An illustration of a ninja jumping across three mountains of increasing size labeled 'Detection,' 'Scale,' and 'Trust,' symbolizing progress in an organization's data observability journey.

Detection

Engineers

Be the first to know when something breaks.

ML-powered anomaly detection
Validations / Expectations
Operational Monitoring
Coverage (bulk + automation)

Scale

The entire data team

Collaborate, create ownership, and shorten response times.

Incident management
Impact and root cause
Performance metrics
Governance

Trust

Data consumers & the org

Make data health part of consumers’ workflow and deliver reliable data products

Data health scoring
Non-technical test config
Business users alerts
Catalog-as-code

Watch demo videos

Developing the Observability Solution We've Always Wanted

Community built & loved

Powered by a community of thousands. Join our community to learn and share.

Secure by design

Elementary is SOC 2 compliant, and does not access or process raw data.

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Works with your stack

Integrates with your warehouse, orchestration tools, BI, code repos, and more.

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Next Steps

Our experts will guide you through onboarding, sharing knowledge
and best practices from thousands of users.

Book a demo

Get on a call with our experts to explore how we can assist you.

Free trial / PoC

No commitment needed. Work with our team to define your goals.

Onboarding

We’ll share best practices & knowledge based on thousands of users.