The trust system for your data. Rust-based control plane for warehouse pipelines — branches, replay, column-level lineage, compile-time safety, per-model cost attribution. Keep Databricks or Snowflake. Bring Rocky for the DAG.

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 resilient, fault‑tolerant telemetry analytics pipeline designed to validate, benchmark, and stress‑test high‑frequency sensor data streams under real‑world failure conditions. Includes chaos testing, DLQ repair, GPU‑accelerated ingestion, and end‑to‑end reliability validation for motorsport‑grade telemetry environments.

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

Real-time data quality screening API — PASS / WARN / BLOCK in <10ms. Schema drift, null spikes, type mismatches, outlier detection. Python SDK.

An MCP server that tests your MCP servers for breaking changes

Esta é uma ferramenta para monitorar alterações no schema de banco de dados PostgreSQL, detectando automaticamente alterações estruturais que podem interromper pipelines ETL, integração de dados ou aplicativos downstream.

Schema-on-read toolkit for ingesting XML sources with structural drift into a lakehouse architecture.

Detect API schema drift before your integrations break.

Schema drift detection API for data pipelines

Database schema drift detector for polyglot Node.js stacks — snapshots and diffs MySQL and MongoDB schemas over time.

Python CLI for detecting breaking data contract changes across APIs, databases, and files before production.

Infer robust Pydantic v2 models from messy, evolving JSON streams

Detecting errors and anomalies in structured data using automation

Observability platform for dbt and Airflow stacks: schema drift, freshness and row-count anomaly detection, SQLite metrics history, self-contained HTML dashboards, structured Slack alerts

Define data pipelines in YAML, execute them asynchronously with Celery, and trace every output column back to its source through automatic lineage graphs. Ships with schema drift detection, JWT auth, HMAC-signed webhooks, immutable audit logs, and a 10-panel Grafana dashboard. 9 Docker services, 206 tests, full CI/CD, live demo.

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

Agentic ETL orchestrator — monitors data sources, detects schema drift, and auto-heals pipelines

Ready-to-use API monitoring configurations for Rumbliq — detect schema drift in third-party APIs

📊 Analyze data collection issues to distinguish true trends from measurement errors in analytics for accurate decision-making.