Power AI & Analytics Success with Dependable Data

Ensure reliable data for AI and analytics — monitor pipelines in minutes with our end-to-end tools. Trusted by 5,000+ data and analytics engineers.

A Dev-first data quality platform

Monitor data pipelines in minutes, in your dbt project. Elementary is built for and trusted by 5000+ analytics and data engineers.

A user interface showing data quality monitoring with "Pass" and "Fail" indicators for data metrics.

AI-powered observability

Elementary uses AI agents to automate the manual
work — so your team can focus on what actually moves the business forward.

Join 1000+ data teams who trust Elementary

Operationalize Data Quality

From detection to resolution, Elementary helps you reduce noise, respond faster, and improve over time—with AI agents that eliminate the busywork and drive efficiency.

Cut the Alert Noise

Simplified alerting and proactive detection help reduce noise and ensure teams only see what truly matters.

A user interface for configuring anomaly settings, showing a graph with a training period and a detection period for identifying spikes and drops in data.

Resolve Issues Faster

Triage and resolve incidents faster by automatically escalating high-priority issues to the right people and driving ownership.

A user interface displaying "Related assets" for data lineage, with a popup showing a "Failure" notification for a unique test on 'customer_id'.

Track and Report Quality

A centralized solution with reporting drives visibility, accountability, and continuous improvement across teams.

A dashboard showing data health with a total health score of 67%, total tests, and quality dimension scores for completeness, uniqueness, and freshness.

Automate observability tasks

AI-powered workflows handle coverage, resolution, and metadata—creating fix-ready pull requests for your team to approve.

Monitoring

Cut the Alert Noise

Simplified alerting and proactive detection help reduce noise and ensure teams only see what truly matters.

A user interface for configuring anomaly settings, showing a graph with a training period and a detection period for identifying spikes and drops in data.
Response

Resolve Issues Faster

Triage and resolve incidents faster by automatically escalating high-priority issues to the right people and driving ownership.

A user interface displaying "Related assets" for data lineage, with a popup showing a "Failure" notification for a unique test on 'customer_id'.
Accountability

Track and Report Quality

A centralized solution with reporting drives visibility, accountability, and continuous improvement across teams.

A dashboard showing data health with a total health score of 67%, total tests, and quality dimension scores for completeness, uniqueness, and freshness.
Efficiency

Automate observability tasks

AI-powered workflows handle coverage, resolution, and metadata—creating fix-ready pull requests for your team to approve.

We solve the hardest AI
and analytics problem

Your investment in AI and analytics is only as good as the quality of the data feeding it. Bad inputs lead to broken models, hallucinations, and decisions no one can trust.

Data quality is the #1 blocker to AI success and data product adoption.

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Success stories from leading data teams

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"Instead of manually defining thresholds for every dataset, Elementary  automatically detect spikes, drops, and freshness issues across all business units—without redundant configurations."

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"Each stakeholder can open the screen in the morning, get a quick overview of the data — and feel confident in its quality"

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"With Elementary, we’re testing, tagging, and producing a ton of high-quality metadata — everything we need to establish that golden standard of trust."

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Built for Enteprise

A dedicated team you can count on

When the tool is critical, you want a team of experts you can work with to ensure success.

Org-wide visibility

Know who’s doing what, where, and when.

Scale

Built to support complex data stacks and large organizations.  Visual doesn’t fit the message.

Alert routing with context

Connect lineage, owners, and issues to notify the right people.

Secure by design

SSO, SCIM and RBAC to meet compliance.

The Data Observability Tool You’ll Actually Use

See how Elementary covers the entire data quality lifecycle—test coverage, incident triage, metadata, and performance. All in one platform, designed to work the way your team does.

A dashboard showing a list of data tests with "Failure" status for various tables and columns.

Data Quality Tests

Manage dbt tests, Elementary tests and custom SQL tests from one place.

A data testing results dashboard displaying "Failure" status for two tests and a graph illustrating "Automated Volume" over time.

Anomaly Detection

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

A flow diagram illustrating data models such as "stg_customers," "stg_orders," and "customers," connected to show data lineage.

Data Lineage

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

A clean user interface showing an "Incident Management" dashboard with a list of open, acknowledged, and resolved data incidents.

Incidents & Alerting

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

A dashboard showing data health with a total health score of 67%, total tests, and quality dimension scores for completeness, uniqueness, and freshness.

Data Health Scores

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

A performance dashboard displaying data model execution times and status, with a graph showing "Success" over time.

Performance & Cost

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

A dashboard displaying a catalog of data models with descriptions, tags, and "Success" status indicators.

Data Catalog

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

Data Quality Tests
A dashboard showing a list of data tests with "Failure" status for various tables and columns.

Data Quality Tests

Manage dbt tests, Elementary tests and custom SQL tests from one place.

Anomaly Detection
A data testing results dashboard displaying "Failure" status for two tests and a graph illustrating "Automated Volume" over time.

Anomaly Detection

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

Data Lineage
A flow diagram illustrating data models such as "stg_customers," "stg_orders," and "customers," connected to show data lineage.

Data Lineage

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

Incidents & Alerting
A clean user interface showing an "Incident Management" dashboard with a list of open, acknowledged, and resolved data incidents.

Incidents & Alerting

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

Data Health Scores
A dashboard showing data health with a total health score of 67%, total tests, and quality dimension scores for completeness, uniqueness, and freshness.

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
A performance dashboard displaying data model execution times and status, with a graph showing "Success" over time.

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
A dashboard displaying a catalog of data models with descriptions, tags, and "Success" status indicators.

Data Catalog

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

Integrates with everything in your stack

Can't find what your looking for?
Tell us and we'll get right on it.

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Thousands of teams use Elementary OSS in production

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Richard Bedwell
Lead data engineer, Hippo

“Great tool you have here, more annoying that i've only just discovered it rather than anything else”.

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Thijs Bongertman
Head of Data at Circus Kitchens

 “I've just implemented Elementary today, to see whether it can bring added value to our pipelines. Especially with regards to observability and data quality. Spoiler Alert; the implementation was a gazillion times easier than Piperider”

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Juliette Duizabo
Head of Data at Photoroom

“I have been using Elementary for a while in my previous company, and I am starting to set it up in my current company. I am a very big fan!”

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Daan Luttik
Chief Technology Officer at Techonomy

“The Elementary reports are insane!”

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Phyllis Helton
Data engineer at Cru,

“I just added the elementary package to one of our projects yesterday and am wondering why we never did this sooner.”

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Ben Morris
Senior software engineer, Alchera Technologies

“Loving Elementary, we’re open source users and we currently building out our anomaly testing.”

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Jan Gerrit Hölting
Head of Data at HIVED

”being in a very real-world ops-heavy business, we're using Elementary to ensure that we're being alerted if our metrics diverge, usually because some of our assumptions on real-world processes have changed”

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Robert Filtzkowski
Senior data engineer, Booz Allen

“Been using Elementary w/ DBT Core for the past ~8 months and love the product.”

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Victor Mattos
Sr. Data Engineer, Petvisor

“Recently added Elementary to our DQ
stack. It has been a game changer!! We've been able to respond preventively instead of reactively.”

See Elementary in Action

No fluff. No generic pitch.
Get a walkthrough of the platform and see how we help teams deliver data quality with AI-powered efficiency.