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Centralizing Business Logic with Ontology-Based Semantic Layers

Semantic layers have become a standard requirement for modern data architectures, creating a unified view of data that simplifies management and ensures consistency across an organization’s data architecture. By standardizing business logic and data relationships, they ensure consistent data usage and promote self-service analytics across the organization. Ontology-based semantic layers

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Semantic Data Mesh for Scalable Data Management

Data mesh is an innovative data architecture that challenges traditional centralized approaches, like data warehouses and data lakes, by decentralizing data ownership and management. This approach is particularly useful for large organizations that struggle with scalability, data bottlenecks, and the need for real-time data access. To successfully implement a data

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Use Databricks Notebook to Model an Intelligent Semantic Layer

Timbr’s native integration with Databricks enables fully declarative definition of semantic models, so business context, relationships and business rules are defined as part of the data pipeline within a single development environment. This integrated approach allows for a more dynamic and context-aware data quality assurance, ensuring data integrity and consistency with business logic,

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Timbr Seamlessly Integrates with Databricks’ Unity Catalog

Timbr Intelligent Semantic Layer integrates with Databricks’s Unity Catalog Introduction The Timbr Platform, combined with Databricks, offers a powerful solution for creating and utilizing scalable semantic graph data models accesible in SQL for big data environments to efficiently integrate, manage, and share data across teams and business functions. This article

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Model a Timbr SQL Knowledge Graph in just a few minutes and learn how easy it is to explore and query your data with the semantic graph

Model a Timbr SQL Knowledge Graph in just a few minutes and learn how easy it is to explore and query your data with the semantic graph

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