Leading Semantic Layers Compared > AtScale Comparison Report
Download the full report if:
- Your query responses take more than a second
- You have a semantic layer, but it doesn’t support advanced functions
- Analytics performance is slow and depends on the underlying data source
- Your cloud compute costs are increasing
- Your analytics stack can’t scale with your data
- You pay an additional source cost for every query
- You have more than one BI tool but need seamless connectivity to all
- You want a trusted data source for downstream AI applications
Executive Summary
Kyvos is a true semantic performance layer that simplifies analytics, improves query execution, and establishes data trust for leading global enterprises. The platform’s AI-powered smart aggregation leverages machine learning and advanced algorithms to create massively scalable data models without any speed constraints. Kyvos also enables cost-effective end-to-end analytics with its patented analytical aggregate cache and a native visualization layer.
AtScale is a universal semantic layer that delivers business metrics independently from BI tools and cloud data platforms. However, the platform isn’t built to handle analytics on large complex datasets due to its reliance on underlying source systems for performance and has limited data modeling options.
This document explains Kyvos’ advantages over AtScale to help choose the right semantic layer for enterprise AI and BI initiatives.
A Quick Comparison
| Feature | Kyvos | AtScale |
|---|---|---|
| Data Modeling | ||
| Data Models | ROLAP, MOLAP, HOLAP | Virtual HOLAP models |
| Dynamic Time Series Calculations | Fully supported | Limited |
| Quick Data Modeling | Yes | No |
| Dimension Attributes | Fully supported | Limited |
| Calculated Measures | Fully supported | Limited |
| Calculated Members | Supported | Not Supported |
| Querying Costs | ||
| Additional Source Cost | No | Yes |
| Native Visualization | Bundled | Not available |
| Analytical Aggregate Cache | In-built | Not available |
| Partitions | Full flexibility to users | Limited |