Weiser

Data Quality Framework

Introduction

Weiser is a data quality framework designed to help you ensure the integrity and accuracy of your data. It provides a set of tools and checks to validate your data and detect anomalies. It also includes a dashboard to visualize the results of the checks.

Installation

To install Weiser, use the following command:

pip install weiser-ai

Usage

Run example checks

Connections are defined at the datasources section in the config file see: examples/example.yaml.

Run checks in verbose mode:

weiser run examples/example.yaml -v

Watch the CLI Demo

Compile checks only in verbose mode:

weiser compile examples/example.yaml -v

Run dashboard

cd weiser-ui
pip install -r requirements.txt
streamlit run app.py

Watch the Dashboard Demo

Configuration

Simple count check defintion

- name: test row_count
  dataset: orders
  type: row_count
  condition: gt
  threshold: 0

Custom sql definition

- name: test numeric
  dataset: orders
  type: numeric
  measure: sum(budgeted_amount::numeric::float)
  condition: gt
  threshold: 0

Target multiple datasets with the same check definition

- name: test row_count
  dataset: [orders, vendors]
  type: row_count
  condition: gt
  threshold: 0

Check individual group by values in a check

- name: test row_count groupby
  dataset: vendors
  type: row_count
  dimensions:
    - tenant_id
  condition: gt
  threshold: 0

Time aggregation check with granularity

- name: test numeric gt sum yearly
  dataset: orders
  type: sum
  measure: budgeted_amount::numeric::float
  condition: gt
  threshold: 0
  time_dimension:
    name: _updated_at
    granularity: year

Custom SQL expression for dataset and filter usage

- name: test numeric completed
  dataset: >
    SELECT * FROM orders o LEFT JOIN orders_status os ON o.order_id = os.order_id
  type: numeric
  measure: sum(budgeted_amount::numeric::float)
  condition: gt
  threshold: 0
  filter: status = 'FULFILLED'

Missing values check

- name: customer data quality
  dataset: orders
  type: not_empty
  dimensions: ["customer_id", "product_id", "order_date"]
  condition: le
  # Allow up to 5 NULL values per dimension
  threshold: 5
  filter: "status = 'active'"

Anomaly detection check

- name: test anomaly
  # anomaly test should always target metrics metadata dataset
  dataset: metrics
  type: anomaly
  # References Orders row count.
  check_id: c5cee10898e30edd1c0dde3f24966b4c47890fcf247e5b630c2c156f7ac7ba22
  condition: between
  # long tails of normal distribution for Z-score.
  threshold: [-3.5, 3.5]

Contributing

We welcome contributions!

License

This project is licensed under the Apache 2.0 License. See the LICENSE file for more details.

Project links

Data verified by PyPI on Aug 18, 2026
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Key dates

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  • Released:
Newer release available (0.3.1)

1 maintainer

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Avatar for pacofvf from gravatar.com pacofvf

Credits

Author/Maintainer: Paco Valdez

GitHub Statistics

Data verified by PyPI on Aug 18, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Tags

data-quality data-validation data-testing sql yaml llm ai eval

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