etl-testing

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

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

Collections of SQL queries and databases for ETL testing

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.

Python tool for automated dataset reconciliation and data migration validation.

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

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

Quality Engineering portfolio focused on Data Quality, ETL testing, PostgreSQL, SQL, reconciliation, large-volume validation and CI/CD.

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

Migration test framework

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

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

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

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

Database testing with Python, pytest and PostgreSQL: schema, constraints, SQL business rules, data quality and ETL reconciliation, CI on PostgreSQL 15-17

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.

A collection of reusable Database Validation Frameworks, covering database testing, data migration validation, ETL validation, data integrity, and source-to-target reconciliation.

Rust CLI that diffs two CSV exports by key: 2M rows in 2.8s, per-column change breakdown with counted transitions, number/null normalisation, reproducible benchmark included.

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.