ValidateLite: A lightweight CLI for database schema validation and data quality checks. Ideal for CI/CD, ETL, and data pipelines.

To test the viability of a resilient analytical pipeline for clinical and sports health telemetry

A deep-dive analytics article on why most KPI “trends” are actually measurement artifacts. Covers the fake-trend taxonomy (schema drift, coverage loss, time shifts, backfills, dedupe, sampling, mix change), a Trend Courtroom evidence protocol, segment invariance tests, and copy-ready checklists + a Trend Report Card for decision safety.

A technical comparison of two modern data‑engineering approaches for processing customer, orders, and product datasets through an MDM‑aligned pipeline.

A daily ETL pipeline for retail sales data using Python, Pandas, PostgreSQL, and Airflow 3.x. Handles schema drift, cleans and aggregates data, and loads it into a Postgres DB. Includes modular scripts and an Airflow DAG for full orchestration.

Detecting errors and anomalies in structured data using automation

Infer robust Pydantic v2 models from messy, evolving JSON streams