Use Case
Databricks Semantic Model
Timbr transforms Databricks into a scalable knowledge graph, combining intelligent semantic modeling and relationships with Databricks’ powerful analytics capabilities. Timbr enables the fully declarative definition of semantic models directly within the Databricks notebook, allowing users to define and manage ontologies as part of their existing workflows.
Databricks data teams also access the semantic model from the Unity Catalog, to benefit from no-JOINs queries and an easier to manage Databricks environment.
BEHIND THE USE CASE
Timbr-Databricks integration offers a powerful solution for creating and utilizing scalable semantic data models in big data environments to efficiently integrate, manage, and share data across teams and business functions.
REFERENCE ARCHITECTURE
Timbr embeds as a semantic layer within Databricks Lakehouse creating a seamless, connected data environment that enables businesses to simplify workflows and unlock actionable insights. Databricks data zones transform into a single semantic graph to easily query complex relationships.
TUTORIAL VIDEO
Creating a Databricks Semantic Lakehouse is a simple, straightforward process. This video shows how to use Databricks as a data source in Timbr, map Delta Tables to the semantic data model, and how to explore the data as a connected graph and query the data both in Timbr, and in a Databricks notebook.
BEHIND THE USE CASE
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.
Accelerate Databricks' Data Engineering with
Fully Declarative Definition of Semantic Models
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, and offers several 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.
Leverage Unity Catalog with the Semantic Model
Databricks’ users love the native integration feature with Timbr. The virtual semantic data model is accessed directly in Databricks to query the business concepts instantly from the notebook. Besides querying, Timbr also exposes the metadata in the Unity Catalog for convenient governance, and users can enable SSO from Databricks to Timbr to align security and access controls.
Databricks Data Explorer enables users to explore the semantic model concepts integrated into Unity Catalog:
Users can query the semantic model in SQL & Python:
