§ Capability · Data observability

dbt-native testing.

Tests authored, run, and surfaced inside the dbt project itself — same codebase, same CI run, same pull request as the models.

Buyer importance · Critical

Tools with this 7 of 21

Open-source options 4

Across clusters 2

Last updated 2026·05·01

What this is

What counts as dbt-native testing?

These tools live in the dbt codebase. Tests are defined alongside models, run by the same dbt invocation, and can fail the same pull requests. Useful when the team's gravity is in the dbt project and analytics engineers own data quality. Trade-off: anything upstream of dbt — raw ingestion, application writes, streaming sources — is not directly observed.

Tools with this capability

7tools, grouped by primary cluster.

Cluster · Quality & testing 5 tools

Datafold

SaaS / Self-host

Pre-merge data diffing and column-level lineage — the tool that shifts data quality left into the pull request.

Pricing

From $799/custom

First strength

Pre-merge data diffing is genuinely category-defining; no competitor does this as well

dbt-expectations

Metaplane (Datadog)

OSS Self-host

Open-source dbt package adding 50+ Great Expectations-style assertions as native dbt tests that run in your own warehouse.

Pricing

OSS · free

First strength

Free and Apache-2.0 with no paid tier, no SaaS, and no lock-in — the only cost is your own warehouse compute

Elementary

Elementary Data

OSS SaaS / Self-host

The dbt-native observability layer — tests, anomaly detection, and lineage that live inside your dbt project.

Pricing

OSS · free

First strength

Fully open-source core is genuinely production-grade, not a trial ramp to a paid tier

Metaplane

Metaplane (Datadog)

SaaS

ML-powered, no-code data observability for the dbt and warehouse stack with automatic column-level lineage — now Metaplane by Datadog.

Pricing

Published

First strength

ML anomaly detection that accounts for seasonality and trend, with very fast time-to-value (about fifteen-minute setup, alerts within days)

Sifflet

SaaS / Self-host

EU-built full-stack data observability pairing ML-driven monitoring with an embedded catalog and field-level lineage.

Pricing

Contact sales

First strength

Spans all three observability clusters in one product — monitoring, an embedded catalog, and field-level lineage

Cluster · Catalog & discovery 2 tools

DataHub

Acryl Data

OSS SaaS / Self-host

Apache-2.0 metadata platform with a serious managed counterpart — strongest event-driven architecture and column-level SQL lineage in OSS.

Pricing

OSS · free

First strength

Best-in-class column-level SQL lineage parser (SQLGlot-based, benchmarked at 97–99% accuracy on standard corpora)

OpenMetadata

Collate

OSS SaaS / Self-host

Apache-2.0 unified metadata platform with a deliberately simple stack — discovery, lineage, quality, and contracts in one project.

Pricing

OSS · free

First strength

Highest connector count in the OSS catalog space (120+) — particularly strong on dashboards, ML, and pipeline systems

Compare tools with this capability

Head-to-head, side by side.

Datafold vs dbt-expectations Datafold vs Elementary Datafold vs Metaplane Datafold vs Sifflet DataHub vs OpenMetadata dbt-expectations vs Elementary dbt-expectations vs Metaplane dbt-expectations vs Sifflet Elementary vs Metaplane Elementary vs Sifflet Metaplane vs Sifflet

Other capability hubs

Drill into a different capability.

ML anomaly detection → Pre-merge diffing → Data contracts enforcement → Circuit-breaker support → Business glossary → PII auto-classification → Column-level lineage → OpenLineage support →

How this list is built.

Inclusion here is one boolean on each tool's structured profile — if a tool you'd expect is missing, the field is recorded false or not yet verified, never an editorial call. See the methodology for how each field is sourced.