Music Production Data Lab
Public-safe data modeling project turning semi-structured music production notes into structured CSV data, a relational model, SQL queries, a Python build workflow and a Power BI reporting layer.
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Project purpose
This project demonstrates how real-world domain knowledge can be transformed into a small, documented data product.
The source domain is a music production setup, but the portfolio focus is data and process analysis:
- turn semi-structured working notes into clean tabular data
- define stable IDs, categories and relationships
- separate public-safe sample data from private source material
- build a relational model with SQLite and SQL
- validate data quality with reproducible checks
- prepare a Power BI reporting layer for communication and review
Workflow
Unstructured notes
-> curated public-safe CSV files
-> documented data model
-> SQLite schema and database build
-> SQL analysis and data-quality checks
-> Power BI overview dashboard
Current portfolio artifacts
| Artifact | What it shows |
|---|---|
| CSV schema | Public-safe table structure and field documentation |
| Data model | Conceptual model, entities and relationships |
| SQLite model notes | Relational preparation and database design notes |
| Python import notes | Reproducible CSV-to-SQLite build workflow |
| Power BI plan | Planned dashboard pages, relationships and measures |
| Publication policy | Public/private boundary and portfolio safety rules |
Data model focus
The central modeling challenge is the relationship between equipment, music references, soundchains and practical workflows.
The current public model includes four main CSV tables:
| Table | Role |
|---|---|
equipment_public.csv |
Public-safe equipment dimension table |
music_references_public.csv |
Reference artists, sound axes and learning goals |
soundchains_public.csv |
Workflow or signal-chain concepts |
soundchain_equipment_public.csv |
Bridge table connecting soundchains and equipment |
This makes the project useful for practicing many-to-many relationships, data-quality checks and BI-style reporting preparation.
Power BI overview
The first public-safe Power BI overview page summarizes the current sample model as a small data product.
The .pbix file remains private. Only reviewed public-safe screenshots are published.
What this demonstrates
- structured data modeling from messy source material
- public/private data separation
- relational thinking with stable identifiers and bridge tables
- SQL and Python build workflow documentation
- data-quality awareness
- Power BI dashboard preparation
- clear portfolio communication for Data/BI and process-analysis roles
Related DataTideHH project pages
- Network Operations Data Lab — public-safe operational IT data, Python, SQL and data-quality workflow
- Spring Boot Process API Basics — small Java/Spring REST API for structured process-check data
Next steps
The next useful project steps are:
- refine the Power BI overview into a small multi-page dashboard
- add one exported reporting dataset from SQL queries
- document the dashboard interpretation for a recruiter or technical reviewer
- keep the project aligned with the broader DataTideHH portfolio structure