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A semantic layer is a business abstraction derived from the technical implementation layer to uniformly maintain business logic. It frees business users from concerns about the technical complexity and implementation of the underlying data source.
Kyligence provides a central platform that integrates different data sources including:
• Databases
• Data warehouses
• Cloud storage
• Big data
• Streaming data.
Thanks to its standard SQL/MDX interface, IT personnel can eliminate data silos caused by traditional disparate systems, define standardized metrics, and provide a centrally governed data mart. All of this enables business users to easily pull together the data they need to make better data-driven business decisions.
Kyligence's unified semantic layer allows different business departments within a company to share the same business logic without developing semantic information separately with their BI tools and applications
Kyligence's semantic model can be exposed as a table similar to a relational database through an SQL (ODBC or JDBC) interface, or as an XMLA-compatible data source with semantic information, which can be queried with MDX. In this way, customers can query a unified business logic, regardless of the BI tools or applications they use.
Kyligence processes data based on Spark's distributed architecture, with the AI-based engine, automatically recognizing users' query modes, conducting computation at the backend, and storing data in the low-cost cluster resources. The aggregate data is automatically created and stored in big data clusters or cloud-based object storage.
Kyligence helps your organization create a unified and common semantic business model to map complex data into clear business terms. The dimensions, measures, and hierarchies familiar to business personnel are synchronized with mainstream BI tools through a unified semantic layer to create a unified and reusable set of business semantics.
Kyligence's business semantic layer supports Excel, Power BI, Tableau, and MicroStrategy.
Business users can directly use the metrics centrally defined on the Kyligence semantic layer through their BI tools without having to care about the complex computing logic of underlying data sources.
IT teams or data analysts can easily define time intelligence metrics to provide business users with straightforward insight into business performance. These metrics include:
• YOY (year-on-year),
• Comparison to the last statistical period, and
• YTD (year-to-date)
Kyligence's powerful multi-dimensional analytics engine makes it easy for companies to handle complex analysis scenarios involving many-to-many relationships, multiple fact tables, semi-additive measures, etc.
By concentrating distributed BI analysis loads on the platform, Kyligence allows enterprises to reduce data security risks created by the spread of data across different business systems. User and data access management can be centrally configured at the data asset layer of the platform and carries over to all business applications at the upper layers. All of this means IT personnel does not have to configure additional data access controls for downstream systems.
With Kyligence, you can configure data access control at the project-level, table-level, and cell-level by users or user groups, and ensure the access control of detailed semantic entities.
With Kyligence's industry-leading distributed MDX engine, data developers can use MDX query language with a traditional semantic OLAP layer while leveraging a Spark distributed cluster. This greatly improves overall query speed by optimizing query performance on complex metrics across hundreds of dimensions and petabytes of data and allows enterprise teams to enjoy superior and stable query performance.
Additionally, DevOps can have the distributed MDX engine service and its Spark cluster hosted as needed to reduce operation and maintenance costs.
A comprehensive guide to the Semantic Layer and industry’s solutions
Learn the evolution of cloud analytics, and how you can achieve performance, efficiency, scalability, and security for your data engineering and analytics workloads on the cloud.
Learn how you can optimize your cloud data lake for both business value and cost, and accelerate your time-to-insight.
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