data-reliability

Data Contracts engine for the modern data stack. https://www.soda.io

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.

re_data - fix data issues before your users & CEO would discover them 😊

This dbt package captures metadata, artifacts, and test results so you can detect anomalies, monitor data quality, and build metadata tables. It powers Elementary OSS and feeds the wider context layer used by Elementary Cloud’s full Data & AI Control Plane.

Code review for data in dbt

Metrics Observability & Troubleshooting

Installer for DataKitchen's Open Source Data Observability Products. Data breaks. Servers break. Your toolchain breaks. Ensure your team is the first to know and the first to solve with visibility across and down your data estate. Save time with simple, fast data quality test generation and execution. Trust your data, tools, and systems end to end.

Swiple enables you to easily observe, understand, validate and improve the quality of your data

⚡ Prevent downstream data quality issues by integrating the Soda Library into your CI/CD pipeline.

This open-source Terraform provider enables users to seamlessly integrate the Monte Carlo data reliabillity platform into their infrastructure as a code (IaC) workflows.

DataBridge Quality Control

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

Stop AI agents breaking on bad data - Open source data quality validation framework for reliable agent workflows

AI-native open-source data reliability platform. Trust Score for every dataset.

Data Quality Tool

Data Reliability Index is a Python package for attaching trust metadata to data, scoring reliability, and filtering records through explicit policies before they are used in analysis, APIs, or pipelines.

Production-style data pipeline reliability lab with failure injection, data contracts, idempotency, SLA monitoring, incident detection, runbooks, and postmortems.

Autonomous data quality and lineage reliability mesh with anomaly detection, reinforcement-learning remediation, and a lineage-aware control plane.

ML anomaly detector for data quality timeseries (Isolation Forest + LSTM Autoencoder + drift detection)

A practical checklist for identifying fragile Excel spreadsheets, structural risks, and reliability issues in operational workflows.