Raw Data Modeling vs Dimensional Modeling

Developers should learn Raw Data Modeling when working with data ingestion, ETL (Extract, Transform, Load) processes, or building data lakes, as it helps organize raw data for downstream applications like machine learning, reporting, or real-time processing meets developers should learn dimensional modeling when building data warehouses, data marts, or bi systems to enable fast and user-friendly reporting and analytics. Here's our take.

Raw Data Modeling

Nice Pick

Developers should learn Raw Data Modeling when working with data ingestion, ETL (Extract, Transform, Load) processes, or building data lakes, as it helps organize raw data for downstream applications like machine learning, reporting, or real-time processing

Pros

• It is essential in scenarios involving IoT data, log analysis, or integrating third-party APIs, where data arrives in varied formats and requires standardization to enable efficient querying and reduce errors in later stages
• Related to: data-modeling, etl

Cons

• Specific tradeoffs depend on your use case

Dimensional Modeling

Developers should learn dimensional modeling when building data warehouses, data marts, or BI systems to enable fast and user-friendly reporting and analytics

Pros

• It is essential for scenarios involving large-scale data analysis, such as sales tracking, customer behavior insights, or operational metrics, as it simplifies complex data relationships and improves query performance
• Related to: data-warehousing, business-intelligence

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Raw Data Modeling is a concept while Dimensional Modeling is a methodology. We picked Raw Data Modeling based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Raw Data Modeling wins

Based on overall popularity. Raw Data Modeling is more widely used, but Dimensional Modeling excels in its own space.