dbt-Native Data Observability
For dbt users that want to deliver reliable data products.
Loved by Engineers. Empowers Data Consumers. Delivers Clarity to Stakeholders.
.avif)
.avif)
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.avif)
Detection & Coverage
.avif)
Triage and Resolution
.avif)
Report Data Health
.avif)
Governance & Discovery
.avif)
Data Observability that Works.
For Everyone.
Make it Easy for Engineers to Invest in Data Quality
- Tools and syntax engineers already know.
- Leverage existing dbt tests, tags, and configurations-
No duplicate work, no vendor lock-in. - Maintain a single source of truth in the code.
Enable Consumers to Collaborate on Data Quality, Without Coding
- A simple UI for validations, coverage, and triage
- Data health scores per domain
- Data discovery using native catalog and lineage
Perfectly balanced for both technical and non-technical users”
Communicate Data Quality for Confident Decision-Making
- Use the same set of agreed-upon, consistent data to make decisions.
- Ensure alignment with best practices and governance goals
- Maximize ROI from your data & analytics investments
overview of the data — and feel confident in its quality.”
Data & Analytics Engineers
Make it Easy for Engineers to Invest in Data Quality
- Tools and syntax engineers already know.
- Leverage existing dbt tests, tags, and configurations-
No duplicate work, no vendor lock-in. - Maintain a single source of truth in the code.
Data Consumers
Enable Consumers to Collaborate on Data Quality, Without Coding
- A simple UI for validations, coverage, and triage
- Data health scores per domain
- Data discovery using native catalog and lineage
Perfectly balanced for both technical and non-technical users”
Stakeholders
Communicate Data Quality for Confident Decision-Making
- Use the same set of agreed-upon, consistent data to make decisions.
- Ensure alignment with best practices and governance goals
- Maximize ROI from your data & analytics investments
overview of the data — and feel confident in its quality.”
Trusted Data Products
AI-Ready Data
Faster Time to Insights
Built into dbt, integrates
with your stack

Everything you need to deliver reliable data. Easily.
Manage dbt tests, Elementary tests, and custom SQL tests from one place to streamline your data quality workflow.

ML-powered anomaly detection monitors automatically identify outliers and unexpected patterns in your data.

Automated column-level lineage allows you to understand downstream impact and uncover root cause.

Streamline incident management, assign ownership, prioritize issues, notify consumers of impacts, and reduce alert fatigue.

Make data quality accessible to everyone by providing health scores by dimension and integrating scores into BI tools and catalogs.

Track failures and runs of jobs, models and test overtime. Fix performance issues that can cause incidents and create unecessary cost.

Discover and trust reliable data by exploring assets, viewing dependencies and test results, and accessing SQL queries for transparency.

Data Quality Tests
Manage dbt tests, Elementary tests, and custom SQL tests from one place to streamline your data quality workflow.

Anomaly Detection
ML-powered anomaly detection monitors automatically identify outliers and unexpected patterns in your data.

Data Lineage
Automated column-level lineage allows you to understand downstream impact and uncover root cause.

Incidents & Alerting
Streamline incident management, assign ownership, prioritize issues, notify consumers of impacts, and reduce alert fatigue.

Data Health Scores
Make data quality accessible to everyone by providing health scores by dimension and integrating scores into BI tools and catalogs.

Performance & Cost
Track failures and runs of jobs, models and test overtime. Fix performance issues that can cause incidents and create unecessary cost.

Data Catalog
Discover and trust reliable data by exploring assets, viewing dependencies and test results, and accessing SQL queries for transparency.

Why Elementary?
Setting the standard for
data observability
dbt-native, code-first
Aligns with your code-first approach and commitment to engineering excellence in data pipelines.
Bridge the gap, create trust
Enable collaboration between technical and non-technical users, ensure transparency with stakeholders, and meet users where they are.
Own your data
The configuration is managed in your code, the metadata is stored in your data warehouse.
Predictable pricing model
A scalable pricing model designed to scale and support optimal coverage.
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.
“Having benchmarked many other tools in the data observability space, Elementary quickly became my go-to when I changed companies. The tool’s simplicity and effectiveness made it an easy choice”
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Staff Data Engineer, Sorare
"Elementary has enabled us to alert analysts who have engaged with our dbt self-serve capabilities of any test failures leading to significantly faster resolution times. Highly recommend!”
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Analytics Engineer, Gousto
"Elementary was incredibly straightforward, with no major configuration changes needed for our dbt project. After a simple package installation and granting basic read access to the Elementary schema in our warehouse, we were up and running the same day!”
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Architecture / Governance Leader, Flock Safety
"One of the things we liked about Elementary was its ability to scale. We didn’t know how many assets we wanted to monitor, and we didn’t want to be limited by a pricing model based on the number of tables monitored.
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Director Data Engineering, Urban Sports Club