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DataOps

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DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations.

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elementary

elementary-data / elementary

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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.

bigquery snowflake data-warehouse dataops data-analysis redshift dbt data-pipelines data-pipeline lineage data-governance data-lineage analytics-engineer dbt-packages data-observability data-reliability dbt-artifacts
  • Updated Dec 24, 2025
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DataTrustEngineering

datatrustengineering / DataTrustEngineering

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Data Trust Engineering (DTE) is a vendor-neutral, engineering-first approach to building trusted, Data, Analytics and AI-ready data systems. This repo hosts the Manifesto, Patterns, and the Trust Dashboard MVP.

open-source machine-learning dashboard data-validation data-engineering dataops community-driven data-quality risk-management data-governance ai-ethics data-mesh data-trust ai-governance model-governance fairness-monitoring
  • Updated Oct 1, 2025
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github.com/topics/dataops
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open-data