Семантический слой данных DataForge работа с витринами данных и обновление BI-отчётов автоматически

During the webinar, we will present new DataForge functionality that allows you to edit the semantic layer and automatically apply changes to all dependent data marts and BI systems. Learn more about the product at https://dataforg.ru/ You will see how DataForge becomes a single source of truth for analytics: — business terms, KPIs, metrics, and dimensions are stored in a structured registry; — any adjustments to the semantic layer are automatically recalculated into models, data marts, and SQL code; — BI systems (Power BI, Qlik, Tableau, FineBI, etc.) receive the latest data structure without manual modification. We will walk through a detailed scenario: — editing Data Mart directly within DataForge; — updating logical and physical models; — automatic generation of correct SQL/DDL; — transparent application of changes to the target database; How BI reports begin to use the updated model without errors, manual fixes, or KPI discrepancies. We'll show you how DataForge integrates DWH design, semantic management, and delivery automation into a single environment. How lineage, versioning, requirements validation, and visual modeling tools help reduce development time, lower maintenance costs, and ensure data consistency across the entire analytical landscape. This webinar will be useful for data architects, systems and business analysts, BI developers, data engineers, and data governance teams looking for a managed, self-service approach and automated data warehouse updates. Dataforge, data forge, data modeling platform, visual modeling dwh, data warehouse design, enterprise dwh modeling, data warehouse design tool, logical model, physical model, logical data model, physical data model, tables, dimensions, facts, star schema, snowflake schema, semantic layer, semantic layer, registry of indicators, kpi registry, metrics, measures, business glossary, business glossary, data lineage, lineage visualization, data catalog integration, documentation of metrics, data documentation, data governance, data management, master data, mdm, data quality, dq, data validation, data profiling, rpi import, registry of indicators and dimensions, data requirements, drag and drop modeling, visual model design, ddl generation, sql generation, sql ddl, schema migrations, data mart creation, data marts, data marts for bi, bi semantic model, power bi, qlik, tableau, finebi, clickhouse Postgres, PostgreSQL, Greenplum, Starrocks, Oracle, MS SQL Server, Yandex Cloud, On-Premise, Hybrid Data Platform, RLS, Row Level Security, SSO, LDAP, Active Directory, Audit Trail, Monitoring, Observability, Versioning, Model Versioning, Change Approval, Change Management, CI/CD for Data, Dataops, ELT, ETL, Ingestion, Transformation, Processing, Airflow, Dagster, DBT Comparison, Data Modeling Tool vs. DBT, Business Metrics vs. SQL, Self-Service Data, Self-Service DWH, Self-Service Bi, Data Mesh, Data Vault, Kimball, Inmon, DWH Architecture, Lakehouse, Iceberg, Data Platform, Accelerating Bi Implementation, Reducing TCO, ROI DWH, Enterprise Ready, Import-Independent Solution, Russian Registry Software, Multi-Tenant, Role Model, Agile Data Modeling, Time-to-Insight SQL autogeneration from semantics, single source of truth, data preparation for AI, AI-ready data, llm-assistant for analytics, explanation layer for AI.