What this blog covers:
- Learn how Power BI semantic models come with limitations of data processing, scalability, semantic modeling and data platform connectivity.
- Understand why Kyvos is a true semantic layer that allows connectivity with all popular BI or AI tools, analytics cost savings and unprecedented scale and speed of analytics, among other benefits.
- Know how Power BI semantic models compare with and fall short to Kyvos semantic layer.
How Is Kyvos Semantic Layer Better Than Power BI Semantic Models?
Semantic layers built on top of a business intelligence (BI) tool have gained massive popularity in the field of data management. A semantic layer provides a powerful architecture to ensure data consistency and simplify the process of interpreting complex datasets. Businesses need this abstraction layer to have a unified data view for querying and deriving insights quickly from varied datasets. It helps democratize data analytics, so users can access and analyze data, even without any technical knowhow.
There are many new semantic layer solutions, such as Power BI semantic models, coming up in the market. However, they are often incomplete or have certain limitations. Kyvos, on the other hand, is a well-developed solution that helps enhance analytics capabilities, improves decision-making and strengthens data governance.
The following differentiators will help organizations make an informed decision about whether to choose Kyvos semantic layer or Power BI semantic models.
Query Performance
Kyvos semantic layer provides self-service querying, so users can get information from any data source or analytics tools, regardless of the size or complexity of the data sets. Kyvos delivers interactive query responses with high concurrency, supporting more than 1000 users.
On the other hand, the Power BI semantic modeling is essentially an in-memory solution. As a result, the refreshed data passes through the memory, and as the data size increases, the cold cache becomes more unpredictable due to Power BI’s row limitation.
When the number of concurrent users and data size go up, its query performance falters, initiating a fallback to DirectQuery. While the queries don’t fail all the time, their volume does impact performance significantly, making it inconsistent.
Data Scale
Kyvos’ semantic layer is designed to handle and process large, complex data. It can handle terabytes of data without any dependence on the RAM of one or more nodes. Power BI’s semantic models, however, are not a distributed solution designed to scale horizontally. They need more RAM to store and process data as the volume increases.
BI Tool Connectivity
Some enterprises use a single BI tool for their business analytics needs, while others may have a variety of BI tools, depending on their day-to-day business operations, type of industry and customer base. For many of these organizations, BI tool connectivity is a vital parameter for building their business analytics stack.
Kyvos’ semantic layer allows connectivity with popular BI and AI tools (like Power BI, Tableau, MicroStrategy and Excel) through SQL and MDX connectors. In addition, its Excel add-in comes with advanced financial analytical capabilities, such as freeform reporting and template-based reporting, among other benefits. Power BI’s semantic model, however, provides connectivity with BI tools only within the Microsoft universe.
Loading and Refresh Performance
One of the biggest challenges that most enterprises face is slow response times while querying massive volumes of data. Kyvos addresses this challenge by enabling a seamless solution to build semantic models on incremental data without downtime, using the existing query engine. As a distributed solution, it can process data within minutes without any restrictions on the number of measures or columns that businesses can add to the model or limitations on per-query requests fired by users.
On the other hand, the standard Power BI service offers refreshes eight times a day while the Premium Per User subscription refreshes 48 times a day. The tool does not entertain data refreshes, say every 10 or 15 minutes. The refresh time is set at a maximum of five hours. When processing large data sets, if a refresh isn’t complete within five hours, the task is canceled automatically.
Connectivity With Data Platforms
Kyvos offers seamless connectivity with data platforms, including Amazon Web Services, Google Cloud, Microsoft Azure, Cloudera and Apache Hadoop. We also support cloud data warehouses, such as Amazon Redshift, Snowflake, Databricks, Teradata, Google BigQuery, Hive and Oracle RDS.
Power BI’s semantic models are offered with or without Microsoft’s Direct Lake Architecture. Its Direct Lake semantic models can pull data tables only from a single warehouse or lakehouse i.e. OneLake, which doesn’t integrate with non-Azure tools. As a result, business users need another data processing layer. Mirroring other databases in OneLake also needs data replication, which results in increased analytics costs.
The Verdict
Power BI helps build semantic models on top of the Microsoft Fabric. It has not been built as a distributed solution that can solve complex data queries in real time. Kyvos’ universal semantic layer has been purpose-built to handle all formats and volumes of data within an enterprise while its smart aggregation technology makes it easy to extract insights from data sets, reducing time and analytics costs.
For more details, read the full report here.