Модель данных без хаоса: факты, PK/FK и проверка показателей в DataForge

A properly constructed data model is the foundation of any analytical system. It is at this stage that the quality of reporting, the accuracy of calculations, and the ability to scale analytics without constant rework are established. Learn more about the platform: https://dataforg.ru In this video, we walk through the practical process of data modeling in DataForge. You'll see how to define fact tables, set up relationships between entities, and verify that metrics are calculated correctly. The demonstration covers the following topics: • transitioning to working with fact tables; • overview of the main model elements; • visualization settings; • detailing and metric properties; • setting up foreign keys (FK); • setting up primary keys (PK); • creating filters; • validating metrics; • final data model assembly. This video will be useful for BI developers, analysts, data architects, DWH specialists, and anyone involved in designing analytical models who wants to avoid common mistakes during the development phase. DataForge allows you to document your data model, ensure transparency of its structure, and create a unified space for collaboration between business and IT. Learn more about the platform: https://dataforg.ru Timecodes: 00:00 Introduction 00:43 Navigating to tables 01:19 Element overview 02:05 Visualization setup 02:59 Metric details 03:27 FK setup 04:03 PK setup 04:49 Filter setup 06:06 Metric validation 06:39 Data model 07:24 Conclusion data model, data model, fact table, PK, FK, primary key, foreign key, DWH design, DataForge, data warehouse, BI developer, data analyst, data architect, enterprise analytics, ETL, data warehouse, dimensional modeling, Kimball, data governance