Managing Relationships in Modern Data Lakes
Introduction Data lakes provide organizations with a robust solution for storing vast volumes of structured, semi-structured, and unstructured data in a highly flexible and scalable environment. Technologies such as Apache Hadoop, Databricks, Azure Data Lake Storage Gen2, Amazon S3, and Google Cloud Storage are leading this revolution, offering cost-effective ways
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
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
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,
Leveraging SQL Knowledge Graphs for Accurate LLM SQL Query Generation
In a previous post, we discussed the need to include knowledge graphs as an integral part of an enterprise LLM strategy. This post discusses how knowledge graphs can be more confidently used to enrich LLM responses. The ability to efficiently query and extract insights from complex databases is one of
From ETL to Semantic Model: Modeling a Semantic Layer in Databricks with Timbr
Timbr’s native integration with Databricks offers a novel approach that combines the power of ETL processes with the advantages of semantic modeling directly within the familiar Databricks Notebook. This integration provides a unified, fully declarative method for defining ETL pipelines and semantic models, opening up new possibilities to bridge the
