Sdp Test

C+ 75 completed
Testing
unknown / python · tiny
19
Files
3,794
LOC
1
Frameworks
4
Languages

Pipeline State

completed
Run ID
#343531
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
50.03
Framework unique
Isolation
Last stage change
2026-05-10 03:35:17
Deduplication group #50769
Member of a group with 6 similar repo(s) — canonical #39697 view group →
Top concepts (2)
Project DescriptionTesting
Source: Repobility analyzer · https://repobility.com

AI Prompt

Create a declarative unit testing framework specifically for validating Spark Declarative Pipelines, like those used in Databricks Lakeflow. I want the tool to allow users to write unit tests using YAML specifications instead of boilerplate Python test code. Key features should include zero configuration, automatic discovery of `*.unit_tests.yml` files next to models, and support for both SQL and Python models. It must read a `databricks.yml` file to resolve schemas and variables, and the testing process should be runnable simply by calling `pytest databricks.yml`.
python testing spark databricks declarative pytest yaml data-pipeline
Generated by gemma4:latest

Catalog Information

A declarative unit testing framework for validating Spark Declarative Pipelines.

Description

This framework provides a declarative approach to unit testing for Spark Declarative Pipelines, enabling developers to define expected pipeline behavior in a concise, readable format. It automatically compares pipeline outputs against predefined expectations, detecting regressions and data quality issues early in the development cycle. The library integrates seamlessly with existing CI/CD pipelines, allowing tests to run as part of automated build and deployment workflows. Targeted at data engineers and pipeline developers, it reduces manual testing effort and improves confidence in pipeline correctness. By focusing on declarative specifications, it simplifies test maintenance and promotes clear documentation of pipeline contracts.

الوصف

يقدم هذا الإطار نهجاً إعلانيًا لاختبار الوحدات لخطوط أنابيب Spark Declarative، مما يمكّن المطورين من تعريف سلوك الأنابيب المتوقع بطريقة موجزة وسهلة القراءة. يقوم تلقائيًا بمقارنة مخرجات الأنابيب مع التوقعات المحددة مسبقًا، مما يكتشف الانحرافات ومشكلات جودة البيانات مبكرًا في دورة التطوير. يتكامل المكتبة بسلاسة مع خطوط CI/CD الحالية، مما يتيح تشغيل الاختبارات كجزء من عمليات البناء والنشر الآلية. يستهدف مهندسي البيانات ومطوري الأنابيب، ويقلل الجهد اليدوي في الاختبار ويزيد الثقة في صحة الأنابيب. بتركيزه على المواصفات الإعلانية، يبسط صيانة الاختبارات ويعزز توثيق عقود الأنابيب بوضوح.

Novelty

7/10

Tags

unit-testing pipeline-validation declarative-testing data-pipeline-verification test-automation data-engineering-testing

Technologies

pydantic

Claude Models

claude-opus-4.6

Quality Score

C+
74.6/100
Structure
88
Code Quality
75
Documentation
52
Testing
85
Practices
62
Security
84
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Good test coverage (57% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected
  • Properly licensed project

Security & Health

5.3h
Tech Debt (C)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (3)
Repobility · severity-and-effort ranking · https://repobility.com
Apache-2.0
License
1.5%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
79.4%
markdown
15.6%
yaml
2.7%
toml
2.2%

Frameworks

pytest

Concepts (2)

Scored by Repobility's multi-pass pipeline · https://repobility.com
CategoryNameDescriptionConfidence
Repobility analyzer · published findings · https://repobility.com
auto_descriptionProject DescriptionDeclarative unit testing framework for Spark Declarative Pipelines (Databricks Lakeflow).80%
auto_categoryTestingtesting70%

Quality Timeline

1 quality score recorded.

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