What is virtual data model in SAP HAN - SAP HANA?

SAP Term: virtual data model

• Component: HAN

• Component Name: SAP HANA

• Description: SAP A A structured representation of A database views used in the SAP A analytics framework for SAP Business Suite following consistent modeling rules. A virtual data model provides direct access to SAP business data via standard SQL or OData requests. Business data is exposed via well-defined database views, operating directly on the tables of the SAP Business Suite systems and transforming them into consistent and easily understandable views. These views can be consumed directly by rich client UIs e.g. HTML5, SAP Business Objects, Excel without any additional software layer e.g. an ABAP application system in between two-tier architecture.

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• Key Concepts: 
  A virtual data model is a feature of SAP HANA that allows users to create a virtual representation of their data. This virtual representation is created by combining multiple physical tables into one logical table, allowing users to access and analyze data from multiple sources in one place. 
  
  How to use it: 
  To use the virtual data model, users must first create a virtual table in the SAP HANA system. This table will contain the columns and rows of the physical tables that are being combined. Once the virtual table is created, users can then access and analyze the data from multiple sources in one place. 
  
  Tips & Tricks: 
  When creating a virtual table, it is important to ensure that all of the columns and rows are properly aligned. This will ensure that the data is accurately represented in the virtual table. Additionally, it is important to ensure that all of the data sources are properly connected to the virtual table so that all of the data can be accessed and analyzed. 
  
  Related Information: 
  The virtual data model is a powerful tool for analyzing data from multiple sources in one place. It can be used to create reports, dashboards, and other visualizations that can help users gain insights into their data. Additionally, it can be used to create predictive models and machine learning algorithms that can help users make better decisions based on their data.