IFS Analysis Models¶
IFS Analysis Models serve as a powerful tool for organizations looking to leverage their data for business intelligence and analytics. By providing a structured approach to data extraction, transformation, and reporting, these models enable users to gain valuable insights and drive informed decision-making across various functional areas.
There are two products within the IFS Analysis Models:
| Criteria | Analysis Models - Tabular | Analysis Models - Power BI |
|---|---|---|
| Purpose | Interactive, self-service analytics provided as Microsoft Tabular Models. | Interactive, self-service analytics provided as Microsoft Power BI Models. |
| Infrastructure (Self-Hosted) |
On-Prem: SQL Server, SSIS, SSAS Azure: Azure SQL Server, Data Factory, AAS |
Azure: ADLS Gen2, On-Premises Data Gateway, Fabric |
| Site-to-Site VPN | Required | Not required |
| Data Sources | Include IFS Information Sources in Online mode, IFS Information Sources in Data Mart mode, and IALs. | Include IFS Information Sources in Online mode, IALs and any table or view. |
| Data Transformation | Utilizes an ELT process to extract, load, and transform data. Transformations are done as a chain of SQL views. | Data transformations are done within the Power BI models provided, using M-Query and DAX. |
| Customization | The customizations can be done through IFS Cloud Web for the SQL Views and Tabular Models. | The customizations can be done through the Power BI models. |
| Data Loading | Data is pulled from the IFS Cloud database into the self-hosted SQL data warehouse by SSIS or Data Factory. Depending on the requirement, the loading process supports full, incremental, or conditional loading. Data loading can be scheduled through IFS Scheduled Jobs or manually triggered. | Data is pushed from the IFS Cloud database into the self-hosted Data Lake, by a Data Pump Service running in the IFS Cloud. The loading process supports full and incremental loading. Data is automatically updated using a Scheduler service and the defined Max Age of data sources. |
| Logging and Monitoring | Supports logging for data loads, processing, and execution of SSIS packages, providing detailed insights. | Logs provide information on Parquet data source load/refresh history and Analysis Model refresh history. |
| Data Security | This depends on SQL Server and Analysis Service user configurations for security and Row Level Security (RLS) is managed within the model. | Utilizes Azure data lake security and allows Row Level Security (RLS) to be defined directly in the Power BI model. |
| SKU Information | Analysis Models - Self Managed - IC10127 | Analysis Models - Power BI - Self Managed - IC10128 |