zerocode-tdd is a community-developed, free, open-source, outcome-driven automated testing for Data Pipelines, ETL, REST API, Kafka(Data Streams), Databases and Load scenarios. all defined in simple JSON or YAML — with zero coding.

Collections of SQL queries and databases for ETL testing

Generate realistic Synthetic enterprise data for Spark, Pandas, testing, demos, benchmarking, learning, and research.

Python tool for automated dataset reconciliation and data migration validation.

ValidateX is a lightweight, extensible data quality validation framework for Python that helps ensure dataset accuracy, consistency, and reliability with automated reporting and quality scoring.

FastAPI service for generating realistic mock data with chaos injection for pipeline testing

Migration test framework

Professional SQL ETL validation scripts for Data Warehouse QA — Snowflake, Redshift, Oracle | Healthcare, Banking & Hospitality domains

Config-driven ETL testing: source-to-target reconciliation, data quality checks, transformation-rule validation, discrepancy reporting

Healthcare CDC validation, Snowflake migration QA, source-to-target reconciliation, and data quality frameworks

BDD data-quality & on-premise to GCP migration testing framework (behave + BigQuery/DuckDB) with PDF evidence reports and Allure. Runs out of the box on synthetic data.

ETL testing tool

Production-ready data quality test suite for public APIs. 74 tests covering schema validation, null checks, pagination integrity, error handling, and ETL pipeline simulation.

This Python Automation Script could be used to validate data between SRC and TGT in data testing projects

AI-augmented test automation framework — UI, API, ELT data and CI/CD. Selenium, Playwright, REST Assured, SQL, GitHub Actions. Self-directed QA/SDET case study.

ETL pipeline testing suite with PySpark and Pytest — schema validation, business rules, unit and integration tests

Data-centric QA automation framework for validating ETL/ELT pipelines in Big Data environments — YAML-driven, Great Expectations validations, AWS S3 and Delta Lake ready.

A comprehensive ETL testing framework that implements data quality validation using Great Expectations. This project demonstrates best practices in data validation, testing, and monitoring for e-commerce data pipelines, featuring automated data quality checks, BDD testing with Behave, and detailed reporting with Allure.