Add tripleaceme/dbt-anomaly-detector

[tripleaceme]

Description

Lightweight dbt package for data quality anomaly detection using Z-score statistics. Features a dual detection engine: pure SQL (works on all warehouses) and Python/pandas (Snowflake, Databricks, BigQuery).

Link to your package's repository: https://github.com/tripleaceme/dbt-anomaly-detector

Checklist

Real world usage

• (Required): I have been using this package in production and am satisfied with its behavior.

First run experience

• (Required): The package includes a licence file detectable by GitHub, such as the Apache 2.0 or MIT licence.
• The package includes a README which explains how to get started with the package and customise its behaviour
• The README indicates which data warehouses/platforms are expected to work with this package

Customisability

• The package uses ref or source, instead of hard-coding table references.

Packages for data transformation (delete if not relevant):

Not relevant — this is an observability/monitoring package, not a data transformation package.

Dependencies

Dependencies on dbt Core

• The package has set a supported require-dbt-version range in dbt_project.yml. ([">=1.6.0", "<2.0.0"])

Dependencies on other packages defined in packages.yml:

No external dependencies — the package is fully self-contained.

Interoperability

• The package does not override dbt Core behaviour in such a way as to impact other dbt resources (models, tests, etc) not provided by the package.
• The package uses the cross-database macros built into dbt Core where available, such as {{ dbt.type_timestamp() }}, {{ dbt.type_string() }}, {{ dbt.type_float() }}, and {{ dbt.current_timestamp() }}.
• The package disambiguates its resource names to avoid clashes with nodes that are likely to already exist in a project. (Models: detect_anomalies, active_alerts, anomaly_summary; Table: metric_snapshots)

Versioning

• (Required): The package's git tags validates against the regex defined in hubcap/version.py. (Tag: v1.0.0)
• The package's version follows the guidance of Semantic Versioning 2.0.0.

[joellabes]

Hey @tripleaceme, thanks for submitting this! Please fill out the submission checklist in the PR template, and can you confirm that you've been using this in production in your own dbt project for a while already?

[tripleaceme]

Hey @tripleaceme, thanks for submitting this! Please fill out the submission checklist in the PR template, and can you confirm that you've been using this in production in your own dbt project for a while already?

Hey Joel, thanks for the review!

I've been running the original version of this package on Snowflake to monitor metric drift across staging and fact models in a couple of projects to track row counts, null rates, and numeric column stats over time. It's helped me catch issues like unexpected null spikes in source data and row count drops after upstream schema changes.

The v1.0.0 release extends this with a pure SQL engine for cross-warehouse support (tested on PostgreSQL) alongside the existing Python engine, but the core detection logic and metric capture workflow have been in use for a while.

I've updated the PR with the full checklist and all items checked.