I am new to Celonis. I have this dilemma on ‘Why do tables exists when we have OLAP tables’.
What is the purpose of two of them existing?
I know tables are present only in views while OLAP tables in analysis.
What are all the differences or things we can do with tables and OLAP tables seperately?
Best answer by aadil.maqbo11
In Celonis, Tables and OLAP Tables exist to serve different use cases, even though both are used for data visualization. The key difference lies in their functionality, purpose, and where they are used.
| Feature | Tables (in Views) | OLAP Tables (in Analysis) |
|---|---|---|
| Purpose | Operational monitoring with real-time insights | Deep analysis and process mining |
| Customization | Limited formatting and filtering | Highly customizable with PQL, aggregations, and advanced calculations |
| Aggregation | Pre-aggregated metrics (simplified) | Allows dynamic aggregation with OLAP cube logic |
| Interactivity | Basic filtering and selection | Drill-down, pivoting, slicing & dicing of data |
| Data Source | Pre-defined datasets | Uses the OLAP cube for multidimensional analysis |
| Performance | Optimized for quick operational insights | Optimized for detailed exploration of large datasets
|
+2In Celonis, Tables and OLAP Tables exist to serve different use cases, even though both are used for data visualization. The key difference lies in their functionality, purpose, and where they are used.
| Feature | Tables (in Views) | OLAP Tables (in Analysis) |
|---|---|---|
| Purpose | Operational monitoring with real-time insights | Deep analysis and process mining |
| Customization | Limited formatting and filtering | Highly customizable with PQL, aggregations, and advanced calculations |
| Aggregation | Pre-aggregated metrics (simplified) | Allows dynamic aggregation with OLAP cube logic |
| Interactivity | Basic filtering and selection | Drill-down, pivoting, slicing & dicing of data |
| Data Source | Pre-defined datasets | Uses the OLAP cube for multidimensional analysis |
| Performance | Optimized for quick operational insights | Optimized for detailed exploration of large datasets
|
Thank you so much for clarifying this concept.
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