{"id":1738,"date":"2026-06-11T10:03:42","date_gmt":"2026-06-11T10:03:42","guid":{"rendered":"https:\/\/tecnetdati.com\/?page_id=1738"},"modified":"2026-06-15T09:36:23","modified_gmt":"2026-06-15T09:36:23","slug":"data-warehouse-architettura-e-principi","status":"publish","type":"page","link":"https:\/\/tecnetdati.com\/en\/elenco-corsi\/data-warehouse-architettura-e-principi\/","title":{"rendered":"Data Warehouse: Architecture and Principles"},"content":{"rendered":"

Data Warehouse: architecture and principles<\/h1>\r\n\r\n\r\n\r\n

The Data Warehouse<\/strong> \u00e8 una soluzione dati per supportare in modo adeguato i processi decisionali. Dopo oltre vent\u2019anni di esperienze, vanno riconsiderare le scelte fatte in passato, in termini sia architetturali sia di fruizione dei dati, alla luce dei modelli di business emergenti che vedono come prerequisito la connessione 24×7 al sistema informativo. Sul Back End, il Cloud delocalizza la base dati, mentre sul Front End i dispositivi Mobile consentono l\u2019utilizzo delle informazioni ad un bacino di utenza sempre pi\u00f9 ampio. Un altro aspetto importante \u00e8 quello delle tecnologie che supportano i Big Data, strutturati o meno. E\u2019 ormai dominio comune l\u2019utilizzo di Data Lake al posto di Operational Data Store o Aree di Staging: quando \u00e8 meglio l\u2019una o l\u2019altra? E la Data Virtualization quale apporto di semplificazione pu\u00f2 portare all\u2019intera architettura? Il corso, partendo dal ciclo di vita del Data Warehouse, esplora le new technologies available<\/strong> (DW, ETL, ELT, BI) and new information requests coming from the business (from marketing to management control, to Customer Care, etc.) with the aim of identifying the most appropriate answer in relation to the user's needs. The main architectures are also examined, from the classic 2\/3-tier ones, up to the Lambda \/ Gamma-Delta Architecture, highlighting their characteristics, pros and cons and comparing them in terms of usage needs. The objective of the course is therefore to provide a Comprehensive overview<\/strong> especially from the perspective of data structures, their lifecycles, and Data Governance.<\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

\r\n

Contents<\/h2>\r\n\r\n\r\n\r\n

Data Warehousing Environment Architecture<\/p>\r\n\r\n\r\n\r\n

Comparison of different architectures (Data Warehouse, Data Mart, and ODS), comparison of models (SQL, NoSQL, Star Schema, and derivatives)<\/p>\r\n\r\n\r\n\r\n

What are they, what are the principles, when are they useful, and what are the parameters to keep under control?.<\/p>\r\n\r\n\r\n\r\n

On-Premise and Cloud, with a review of the main market offerings (from Amazon to Snowflake).<\/p>\r\n\r\n\r\n\r\n

ETL, ELT and ESB, through to Data Virtualisation.<\/p>\r\n\r\n\r\n\r\n

Problems and techniques for the construction of software component components.<\/p>\r\n\r\n\r\n\r\n

When and how to integrate them, positioning.<\/p>\r\n\r\n\r\n\r\n

Role within the Data Warehousing, Data Catalog environment.<\/p>\r\n\r\n\r\n\r\n

From Business Glossary to Data Catalog, schema derivation, to summary schemas. Metadata and data quality.<\/p>\r\n\r\n\r\n\r\n

Segmentation and user types.<\/p>\r\n\r\n\r\n\r\n

Comparison between traditional PM approach and Agile approach (requirements, analysis methods, Test strategies).<\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

\r\n

Prerequisites<\/h2>\r\n\r\n\r\n\r\n

Basic knowledge of management and business intelligence systems, data, and the software lifecycle.<\/p>\r\n\r\n\r\n

\r\n

Recipients<\/h2>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Development leads<\/p>\r\n\r\n\r\n\r\n

Designers and Specifiers<\/p>\r\n\r\n\r\n\r\n

Analyst<\/p>\r\n\r\n

 <\/p>","protected":false},"excerpt":{"rendered":"

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