dbt-dqlens 0.2.0

Data quality for dbt, without writing tests. Auto-generates dbt tests from DQLens profiling.

dbt-dqlens

Data quality for dbt, without writing tests.

dbt-dqlens brings auto-generated data quality checks into your dbt project. It profiles your models, detects problems (null spikes, orphaned records, schema drift, outliers), and exposes findings as native dbt tests and a queryable model.

You don't write tests. DQLens writes them for you.

Quick Start

1. Install

Add to your packages.yml:

packages:
  - package: vahid110/dbt_dqlens
    version: 0.1.0

Then:

pip install dqlens[duckdb]   # or just: pip install dqlens (for PostgreSQL/MySQL/SQLite)
dbt deps

2. Profile your models

After dbt run, profile your warehouse:

dbt run-operation dqlens_profile

This connects to your warehouse (using your dbt profile), profiles every model, and stores baselines in a _dqlens schema.

3. Generate tests

dbt run-operation dqlens_generate_tests

This creates a _dqlens_tests.yml file with auto-generated tests for every model. Review it, commit it, done.

4. Run tests

dbt test --select tag:dqlens

Your auto-generated tests run as native dbt tests. Failures show up in dbt docs, dbt Cloud, and your CI pipeline.

What it detects

Check	What it catches
Null drift	Null rate increased significantly from baseline
Schema drift	Columns added, removed, or type changed
Orphaned records	FK references to non-existent rows
Empty strings	Columns full of '' that look non-null but aren't
Outliers	Values beyond 1.5x IQR bounds
Row count anomalies	Unusual growth or shrinkage
Freshness	Data that hasn't been updated recently
Pattern violations	Values that don't match detected patterns (email, UUID, etc.)

How it works

dbt run                          (your models build as usual)
    |
dbt run-operation dqlens_profile (DQLens profiles the output tables)
    |
dbt run-operation dqlens_generate_tests  (auto-generates schema.yml tests)
    |
dbt test --select tag:dqlens     (runs the generated tests)

DQLens reads your dbt profiles.yml to connect to the same warehouse. No double configuration.

The dqlens_findings model

Every profiling run materializes a dqlens_findings table in your warehouse:

column	type	description
finding_id	text	Unique identifier
table_name	text	Which model
column_name	text	Which column (null for table-level)
severity	text	HIGH / MEDIUM / LOW
category	text	null_anomaly, schema_change, fk_mismatch, etc.
message	text	Human-readable description
detail	text	Why it was flagged
current_value	text	Current metric value
baseline_value	text	Previous metric value
detected_at	timestamp	When the finding was detected

Query it in your BI tool, build alerts on it, or just SELECT * FROM dqlens.dqlens_findings WHERE severity = 'HIGH'.

Configuration

In your dbt_project.yml:

vars:
  dqlens:
    dqlens_schema: "dqlens"        # where findings table lives
    min_severity: "MEDIUM"          # only store MEDIUM+ findings
    exclude_tables: ["staging_*"]   # skip these models

vs other dbt quality packages

	dbt_expectations	elementary	dbt-dqlens
Auto-generates tests	No	Partial	Yes
Requires writing config	Yes (per column)	Yes (YAML)	No
Drift detection	No	Yes (paid)	Yes (free)
Baseline comparison	No	Yes (paid)	Yes (free)
Outlier detection	No	Yes (paid)	Yes (free)
Pricing	Free	Free + paid cloud	Free

Requirements

• dbt-core >= 1.0.0
• Python with dqlens installed (pip install dqlens[duckdb] for DuckDB)
• Supported databases: PostgreSQL, DuckDB, SQLite, MySQL (Snowflake, BigQuery coming soon)

License

MIT