The typed graph between your code and whichever warehouse, table format, or query engine you've chosen — typed compiler, branches, replay, column-level lineage, compile-time contracts, per-model cost. Adapters: Databricks, Snowflake, BigQuery, DuckDB. Single static Rust binary. Apache 2.0.

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.

Schema drift detection for MCP servers. The lockfile MCP should have shipped with.

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

mysql-mcp — MCP server connecting Claude Code to MySQL. Supports SSH tunnels, SSL/TLS, and multiple databases. 20 tools for schema introspection, query execution, ERD generation, stored procedures, triggers, and events. Includes CLI for connection management.

Zero-dependency Python library for data pipeline observability — audit trails, schema drift detection, and row-level change tracking.

An MCP server that tests your MCP servers for breaking changes

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

Detect API schema drift before your integrations break.

A schema contract guard for data pipelines. Detects, classifies, and reports schema changes in PostgreSQL databases.

Schema drift monitoring system for ETL pipelines on GCP, with a no-billing local demo.

Instant diagnostics for Pandas and PySpark DataFrames: detect schema drift, null spikes, duplicates, skew, risky joins, and pipeline health issues.

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

Detect schema drift and breaking contract changes across APIs, databases, and files. OpenAPI deep diff, CI gate, PR comments.

Schema drift detection API for data pipelines

Detecting errors and anomalies in structured data using automation

Infer robust Pydantic v2 models from messy, evolving JSON streams

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