Netune reads your raw data, shows you the model it implies, and generates the Staging, Data Warehouse and Data Mart layers for you — in the OLAP methodology that actually fits how your business asks questions.
This is the shape of what Netune builds. Raw sources land, staging conforms them, the warehouse models them and the marts serve them — each stage a shade deeper than the last.
Netune keeps the whole path visible. Nothing is generated that you can't inspect, adjust and re-run.
Connect a data lake, a folder of files or an operational database. Netune profiles every column — types, nullability, cardinality, candidate keys and the relationships between tables.
Pick the modeling approach you want to work in, or start from Netune's suggestion based on the shape of your data and the questions you need it to answer.
The schema is drawn before it's built. Netune then writes the staging DDL, the warehouse tables, the keys and the transformation logic that moves data between every layer.
Export the SQL and the mappings to your own engine and orchestrator — and come back to the canvas whenever the sources or the model need to change.
There is no single correct warehouse design. Netune supports eight established OLAP methodologies, applies each one's real modeling rules, and shows you what your data looks like under it before anything is built.
Not sure which one your data is asking for? Read how to choose a data warehouse model — all eight compared, with what each costs and when each one does not fit.
Each layer has one job. Netune writes all three, and keeps the logic between them consistent when something upstream changes.
A faithful, typed landing zone. Nothing is modeled yet — this layer just makes raw data trustworthy.
The model itself, in whichever methodology you selected — with that methodology's own rules actually applied.
Narrow, fast and shaped for the question being asked — derived from the warehouse, so definitions stay consistent.
Data with no database behind it comes in the same way: Netune imports Excel workbooks into SQL Server and JSON files into SQL Server, nested arrays included — the whole file read before a single column is decided, every type shown before it is created, and the rows written in one transaction.
Netune is a web app for designing and automating data warehouses. You point it at raw data, choose a modeling methodology, and it visualizes and generates the Staging, Data Warehouse and Data Mart layers — including the SQL and the transformation logic between them.
No. Netune profiles your data first and suggests the methodologies that suit its shape and your reporting needs. You can compare how the same source data models under each one, then commit to the design you prefer.
A Star schema centers on a single fact table surrounded by its dimensions. A Galaxy schema — also called a fact constellation — holds multiple fact tables that share the same conformed dimensions, so several business processes can be analyzed together without duplicating dimension storage.
Medallion structures a lakehouse into three progressively refined layers: Bronze holds raw data exactly as it landed, Silver holds cleaned and conformed tables, and Gold holds the aggregated, business-level tables that reporting reads from. Each layer can be rebuilt from the one before it.
Yes. Everything Netune produces is inspectable and exportable — staging migrations, warehouse DDL and the transformation mappings — so you can review it and deploy it in your own database engine or orchestrator.
Netune is in active development and access is opening in stages. Request early access and we'll get in touch — tell us about your sources and your target architecture, and we'll prioritize accordingly.
Bring your raw data. Netune will show you the model — and then build it.