Databricks’ Delta Lake has become popular thanks to the wide range of advanced capabilities it brings to datalakes. While Delta Lake excels in gathering and organizing data to enable convenient access, organizations turn to Timbr to facilitate data consumption.
Timbr’s native integration with Databricks’ Unity Catalog, enables creation of semantic data models (ontologies) on Delta Lake, with declarative SQL statements added along the Databricks ETL definition workflow. These models are comprised of abstract concepts embedded with relationships and logic that provide common meaning, classification, and hierarchies to data to accelerate and facilitate consumption.
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Timbr semantic data modeling supports both ontology-based knowledge graphs and multi-dimensional models (OLAP cubes). This integrated approach allows for a more dynamic and context-aware data quality assurance, ensuring data integrity and consistency with business logic. Other advantages:
- Unified data quality management: Data cleaning and validation occur not just during ETL, but continuously as data is accessed and analyzed through the semantic layer.
- Business-context preservation: The semantic model ensures that data transformations and validations align with business meanings and relationships.
- Flexible rule application: Business rules can be adjusted in the semantic model without necessarily altering underlying ETL processes.
- Enhanced data governance: The semantic layer integrates with Databricks Unity Catalog to provide a centralized point for managing data definitions, relationships, and quality rules.
Timbr’s unique capabilities make it a superior choice for organizations seeking to add rich context and relationships to data, transforming Lakehouses into dynamic Knowledge Hubs that provide meaning to data, break down silos, and facilitate comprehensive, insightful analytics.
Learn more about how to model a Timbr semantic data model in Databricks.
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How do you make your data smart?
timbr virtually transforms existing databases into semantic SQL knowledge graphs with inference and graph capabilities, so data consumers can deliver fast answers and unique insights with minimum effort.
