Elementary Data

Elementary Data is a data observability & reliability platform built for the AI era, with first-class, native support for dbt. Its unified control plane combines observability, quality monitoring, governance and discovery so data teams can ship trusted data faster and keep pipelines reliable.

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dbt observability platformdata reliability monitoringcolumn-level lineage trackingAI data quality alertsopen-source data governancedata pipeline anomaly detectiondbt test automation

Features of Elementary Data

Full-stack observability that merges metadata, lineage, logs and validation signals in one contextual engine

Proactive data-quality monitoring for freshness, schema drift and distribution anomalies

End-to-end column-level lineage visualization and impact analysis to pinpoint root causes fast

Centralized dbt test management—built-in, package or custom SQL tests all in one place

AI assistant Ella recommends tests, triages failures, auto-tags governance labels and more

Code-first config: every observability rule lives in version-controlled dbt project files

Smart data catalog helps engineers and analysts find, trust and reuse assets quickly

Native integrations with leading BI tools, cloud warehouses, orchestrators and collaboration platforms

Use Cases of Elementary Data

Data engineers who need instant visibility into dbt-run pipelines and rapid anomaly triage

Growing data teams building a single reliability framework across a multi-warehouse stack

Business analysts validating data freshness before publishing dashboards to stakeholders

Governance teams automating compliance tagging, documentation and data classification

Developers adding data tests to CI/CD so every pull request enforces quality gates

Cross-functional teams exploring lineage to speed up data discovery and decision-making

FAQ about Elementary Data

QWhat is Elementary Data?

Elementary Data is an observability & reliability platform purpose-built for dbt. It monitors data quality, visualizes lineage and keeps your pipelines trustworthy.

QWhat are the main features?

Observability, automated quality checks, column-level lineage, centralized dbt tests, governance workflows and an AI assistant (Ella) that recommends and triages tests for you.

QHow do I deploy it?

Choose open-source self-hosting via a dbt package or the managed Elementary Cloud service—both install in minutes.

QWhich tools does it integrate with?

Out-of-the-box connectors for Snowflake, BigQuery, Redshift, dbt Cloud/Core, Airflow, Dagster, Slack, Jira and major BI platforms.

QDo I need to write code?

Elementary is code-first: you add a YAML block to your dbt repo. Basic dbt and SQL knowledge is enough to get full value.

QHow is data security handled?

The service reads only metadata and query logs—never your raw data—so security boundaries stay intact.

QWho should use Elementary?

Any team running dbt in production that wants faster incident response, higher data trust and lighter governance overhead.

QWhat can Ella, the AI agent, do?

Ella suggests tests, ranks failures by impact, auto-tags PII, answers lineage questions and even drafts Slack alerts for you.

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