Data Architect Tool

A tool for data engineers and architects to model schemas, evaluate data flows, and optimize storage design early.

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About This Tool

The tool enables data modeling by accepting a dictionary of entities, attributes, data types, keys, relationships, and data volumes to produce a normalized schema, a data lineage map, and storage estimates. It is designed for data engineers, data architects, and analytics teams tasked with designing and migrating data platforms, warehouses, or data lakes.
Core logic focuses on formal schema design rules and observable outputs. Required inputs include entities, attributes with types and constraints, keys and relationships, plus row counts and sizes. The system computes normalization status, detects redundancy, and proposes targets (e.g., 3NF/BCNF). Optional features include denormalization recommendations for reporting, indexing guidance, and cross-source lineage analysis.
Outputs are machine-readable and human-interpretable: a schema definition, a normalization score, a data lineage graph, storage estimates, and export-ready definitions in SQL or JSON. The tool does not execute queries or alter databases; it delivers modeling intelligence that teams can apply during design reviews, migrations, and governance planning.
Use cases include designing a data warehouse, planning ETL pipelines, and aligning data models with analytics needs. Unique value comes from combining normalization scoring, storage estimation, and explicit data lineage within a single model, reducing modeling time and enabling proactive governance.

How to Use

  1. Provide inputs: define entities, attributes, data types, nullability, keys, and relationships.
  2. Specify volumes: row_count and average_row_size for storage estimates.
  3. Choose modeling preferences: normalization level (e.g., 3NF) and governance tags.
  4. Run calculation: generate the normalized schema, storage estimate, and lineage map.
  5. Review outputs: export SQL/JSON, and iterate as needed.
How to use data architect tool

Frequently Asked Questions

Find Quick Answers

What inputs are required to run the tool?
At minimum, define one or more entities with attributes, data types, and constraints, including keys and relationships. Provide row_count and average_row_size for storage estimates; optional governance tags and data sources enhance lineage and metadata.
How is storage estimated?
Storage is estimated as row_count multiplied by average_row_size, adjusted by a simple indexing overhead factor. Results are per-table estimates that aggregate to a project total; large variations in row size can be modeled with overrides.
Can it model multi-source data and lineage across systems?
Yes. The tool accepts multiple sources and maps relationships to produce a data lineage graph, enabling impact analysis, convergence planning, and governance tagging across your data landscape.

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