DataOps Lakehouse Platform
Sources land once, are tested at every hop and are served governed to reports, ML and agents, with CI/CD and governance always on.
One governed platform from source to AI. Batch and streaming connectors land every source raw in the bronze layer; versioned Spark and dbt jobs clean it into silver; data tests gate the way to gold. Gold data products feed the warehouse, dashboards, the feature store and vector indexes for RAG and agents. Orchestration and CI/CD run every pipeline as code above it, and catalog, lineage, access and keys govern it from day one.
Point at a component to light its links; select it for what it does here and the practice that makes it right on AWS.
When to use it
- Reports arrive late or disagree, and every team keeps its own copy of the data
- A data platform grew by hand and now needs environments, tests and access as code
- AI pilots stall because the data they need sits outside the governed platform
How NGT delivers it
Built from code, reviewed at a gate by an NGT architect, and handed over with runbooks your team can run.
DataOpsThe same pattern, the way each provider builds it
Each cloud's own framework and guidance.
