The dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.
Open-source data movement for ELT pipelines and AI agents — from APIs, databases & files to warehouses, lakes, and AI applications. Both self-hosted and Cloud.
Deterministic file observation for pipelines — one read-only pass over a directory emits a reproducible JSON manifest of every file's type, metadata, structure, and provenance.
Local-first AI copilot for data quality: 39 detectors, six-dimension scoring, AI repair with human approval, drift engine, cron scheduling with distributed worker pool (failover + parallel), MCP/REST/CLI/Web UI. Apache-2.0.
High-performance, hardware-aware Layer 0 deduplication engine. Uses 4-tier short-circuit logic (Size → Fringe → Full Hash) with xxHash128 to eliminate exact duplicates before they enter your processing pipeline. Python 3.11+, uv, SQLite.
Lightweight self-hosted workflow scheduler in a single Go binary — an open-source Airflow/Azkaban alternative. Embedded SQLite, DAGs (cron + dependencies + backfill), polyglot tasks (shell/Python/SQL/JAR/HTTP), web console, REST API, and a built-in MCP server for AI agents. Linux & macOS.
An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈