Elementary

Control plane for data and AI reliability, combining observability, quality, governance,

Elementary is a data observability and reliability platform built on an open-source foundation. It addresses the problem of fragmented data tooling and unreliable data by bringing metadata, lineage, logs, validations, and health signals into a single context engine. This shared context supports workflows for engineers, business users, and AI agents across discovery, governance, data quality, and observability.

Key capabilities include incidents and alerts, automated pipeline monitoring with ML-based anomaly detection, data quality tests, column-level lineage, a catalog, health scores, performance and cost analysis, and an MCP Server that exposes the context layer to AI tools. Integrations span ETL tools like Airbyte and Fivetran, orchestration platforms such as Airflow and Dagster, warehouses including BigQuery, Databricks, and Snowflake, dbt Core and dbt Cloud, BI tools like Looker and Metabase, and collaboration tools such as Jira and Slack.

Elementary is packaged as Elementary Cloud, with seat-based and environment-based pricing across Scale, Enterprise, and Unlimited editions, plus an AI Layer add-on with credit-based pricing. A 30-day free trial is available, and a self-hosted open-source version can be installed in your own environment.

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