Best 24 Data Quality Tools for dbt (2026)

Testing built into open-source dbt Core: generic and custom SQL data tests run with dbt test, unit tests check model logic on fixed inputs, and model contracts enforce column types and constraints at build time, extended by packages such as dbt_utils, dbt-expectations, and audit_helper.

Managed data and AI control plane by Boomi that installs the Elementary dbt package, whose on-run-end hooks save run results and artifacts to a warehouse schema that Elementary reads through the warehouse connection.

Managed agentic data platform built on OpenMetadata that connects to dbt Cloud through its APIs with an account ID and API token, ingesting jobs as pipelines, with test results from a separate dbt workflow.

Enterprise AI trust platform that ingests dbt Core run info through the Bigeye CLI or Airflow operator and reads dbt Cloud job runs through an API token, linking tables to their last job run.

Data and AI observability control plane that imports dbt Core artifacts uploaded by your pipeline or fetches dbt Cloud artifacts by API, turning dbt tests into monitors and showing failed or skipped models in lineage.

Data observability, quality, and lineage platform that connects to dbt Cloud with a service token to sync models and jobs into its data catalog and run a job failure monitor.

Agentic data trust platform with data quality rules, observability, a data catalog, lineage, master data management, and a built-in AI agent.

Open-source dbt package by Boomi that installs the Elementary dbt package, whose on-run-end hooks save run results and artifacts to a warehouse schema that Elementary reads through the warehouse connection.

Data + AI observability platform that ingests dbt Cloud and dbt Core manifest.json and run_results.json artifacts, alerts on model, test, snapshot, and seed failures, and overlays dbt job status on lineage.

Autonomous data and AI platform that polls the dbt Cloud API every 30 minutes by default, turning job runs and their artifacts into pipeline runs with model, test, and lineage details.

Data quality control layer that reads dbt manifest.json through the Qualytics CLI and creates or updates Qualytics checks from your dbt tests, keyed by each test's unique_id.

Open-source data quality operations center that runs DQOps data quality checks alongside dbt transformations from Python with the dqops package, including from Airflow for dbt Cloud jobs.

Data observability platform by Datadog that connects to the dbt Cloud API with a read-only service token to pull job run durations, models, and lineage, matching dbt models to warehouse tables.

Agentic data quality and observability platform that fetches dbt Cloud model runs and test results on a schedule with a read-only API token, monitored as dbt Model Run and Test Result sources.

AI-native data quality platform that connects to dbt Core and dbt Cloud to ingest dbt test results and keep them over time for anomaly detection, schema change monitoring, and alerts.

Data quality product by Coalesce, built on SYNQ, that connects to dbt Cloud with a read-only service token and collects manifest.json, run_results.json, and catalog.json to track runs and test failures.

AI-powered data operations center with data and pipeline observability, cross-platform pipeline lineage, and automated root-cause analysis.

Autonomous data quality monitoring platform where AI agents monitor, investigate, and report issues across structured and unstructured data.

Zero-code data quality software where AI agents auto-discover rules, validate and reconcile data, and score data trust across sources.

AI data review agent for dbt pull requests that runs in your dbt project, comparing base and current manifest.json and catalog.json to show lineage and data diffs for a pull request.

AI-native data quality platform built on data contracts that ingests dbt manifest.json and run_results.json files, or fetches them from the dbt Cloud API, and maps dbt tests to Soda Cloud checks with alert rules and incidents.

Open-source data contracts engine that ingests dbt manifest.json and run_results.json files, or fetches them from the dbt Cloud API, and maps dbt tests to Soda Cloud checks with alert rules and incidents.

AI-powered data engineering platform that uploads dbt manifest.json files from CI on merges and pull requests, then runs data diffs only for the models a pull request changes (Slim Diff).

Open-source Python data quality framework that runs separately from dbt, validating the tables dbt builds with GX checkpoints as a pipeline step, while dbt-expectations, a separate dbt package, ports its Expectations into dbt tests.