What this blog covers:
- Understand how Kyvos semantic layer compares with Power BI semantic models, with or without Direct Lake architecture.
- Learn how Kyvos delivers high query performance at lesser costs even when data volumes and complexities grow.
- Find more about the key advantages of Kyvos semantic layer over Power BI semantic models in our latest report.
Power BI, with or without Microsoft’s Direct Lake architecture, presents several limitations when dealing with large, complex datasets and thousands of concurrent users and is not ideal when enterprise-grade semantic model capabilities are required.
A universal semantic layer should be used to simplify and speed up analytics. It is designed to improve query performance and establish data trust, without any constraints of scale, connectivity or cost. Kyvos AI powered semantic layer is purpose-built to meet these objectives, and it exhibits many advantages over the Power BI semantic models, regardless of the architecture it’s implemented on. This comparative benchmarks Kyvos semantic layer vs. Power BI semantic model:
Connectivity with Data Platforms
Direct Lake seamlessly connects Power BI to OneLake that is part of Microsoft Fabric, loading the data directly which is better than an import into Power BI or a slower Direct Query option. However, the advantages end right there as using One Lake as a data platform is the only option available.
There is no such platform lock-in with Kyvos. Users can source their tables and views from any cloud or on-premises data platform of choice.
Connectivity with BI/ AI Tools
Kyvos has named connectors via SQL and MDX for all the popular BI tools like Power BI, Tableau and MicroStrategy. A native LangChain connector enables building Gen AI apps using the LangChain framework. Kyvos also provides best-in-class connectivity with Excel via an OAuth2.0 SSO-enabled MDX connector, making reporting in Excel a seamless feature. Multiple connectors ensure that users have freedom to use any BI/AI tools of choice instead of being forced to use Power BI only.
Query Performance
Power BI is an in-memory solution, which is restrained by the amount of memory available. It encounters scalability and performance issues as enterprise data grows, especially with higher user concurrency. When Power BI’s row limits are exceeded, data does not fit in memory or tables- a “fallback” option is triggered to Direct Query. While this prevents queries from failing outright, performance becomes highly inconsistent, slow or erratic.
Kyvos uses intelligent aggregation and multi-level caching with AI-driven self-tuning, which efficiently manages vast data volumes and query loads. Together with load-balancing, Kyvos powers sub-second queries on billions of rows for thousands of concurrent users with no performance degradation.
Data Scalability
Power BI is not a horizontally scalable and distributed solution and requires more RAM to be added—as enterprise data volume grows—to maintain performance. In a test conducted, the load failed when using Star Schema Benchmarking (SSB) dataset at the scaling factor of 1000x (6 billion fact row count).
Semantic models should hold terabytes of data, putting no limits on the model size due to RAM- exactly how Kyvos performs. The documented limit for Power BI Service with a Pro subscription is 10 GB, and with premium, it is 100 GB max. Each model requires reserving 2x memory for data refreshes and additional memory for serving the queries.
Similarly, instance size with Power BI is limited to 400 GB RAM, while Kyvos is a horizontally scalable solution not limited by a single instance.
The Kyvos BI server cluster is processed on a Floating Master approach. Multiple instances of the Kyvos engine form a cluster governed by a coordination master. If there is a failure, its role is assumed by other nodes in the cluster without downtime. Requests are distributed to the appropriate Kyvos BI server – this architecture makes Kyvos highly scalable.
Load and Refresh
Kyvos can refresh every minute and an unlimited number of times every day. Standard Power BI Service has a refresh limit of 8 times a day; and 48 times on the Premium subscription. Jobs get canceled if the refresh is not completed in a 5-hour window in PBI Premium.
The horizontal scaling of compute engine on Kyvos removes any time-limit constraints on load and refresh.
Advanced OLAP Features
Kyvos has out-of-the-box support for all the advanced financial hierarchies and data modeling capabilities. This includes unbalanced and ragged hierarchies, parent-child hierarchies as well as alternate hierarchies with custom rollups. It also incorporates advanced OLAP (MDX) functions, such as Scope, Alter Cube, Default Member, Cubevalue, which are critical to building a high-performance, true OLAP model.
Power BI semantic models present limitations with support for unbalanced and ragged hierarchies being highly complex, achieved only via a complex ETL with custom functions. Alternate or parent-child hierarchies with custom rollups are not supported and neither are other advanced OLAP (MDX) functions.
Data Governance
Power BI semantic capability allows users to import data on Power BI desktop. This can perpetuate huge governance challenges resulting from a lack of control over data and multiple data copies being proliferated.
Kyvos’ superfast query-serving live connectivity does not require data downloads or imports to desktops, ensuring ironclad data governance, while maintaining trust and a single source of truth.
Limitations in Direct Lake Architecture
MS Fabric is envisioned to be the end-to-end analytics and data platform with centralized data storage in OneLake. The Direct Lake architecture allows users to create semantic models directly from OneLake.
This architecture proves to have some limitations:
- Direct Lake semantic models can derive tables only from a single warehouse or lakehouse—which is OneLake. This lakehouse platform doesn’t allow integration with non-Azure tools, for which another data processing layer should be added, or data should be mirrored in OneLake. This creates replication, poor query speed and increased analytics costs.
- Models or their components aren’t shareable, leading to error-prone manual processes when re-creating a model.
- Edits outside the web-based workspace are not allowed.
- Direct Lake is only supported by tables in the semantic model. For tables derived from SQL views in lakehouse, queries fall back to DirectQuery mode.
- Direct Lake tables do not support other table types. like DirectQuery. Import, dual or composite models are not supported.
- Additionally, there is no support for DateTime relationships; calculated columns and calculated tables; and High-precision decimals and money types.
Conclusion
Choosing the right enterprise-grade semantic layer for speed and efficiency is critical for BI/analytics success. Power BI with semantic models, or Power BI using Direct Lake architecture both prove to have limitations that result in situations where analytics performance degrades with increasing data volumes, or user concurrency is limited, and data refresh and query responses take long.
Kyvos, by design, has none of these constraints and provides faster, more comprehensive data modeling with seamless connectivity to all BI tools. The platform offers advanced Gen AI capabilities for true self-serve analytics.
Access the full report for a comparison between Kyvos semantic layer and Power BI semantic models here.