Powerful SQL Ontologies: The Next Generation of Data Modeling
SQL ontologies provide a practical, flexible way to model data, replacing traditional ERD structures with semantic models that simplify querying relationships across data sources, all within a familiar SQL environment. Relationships are first-class entities, allowing for search, reuse, and exploration based on their structure. These relationships carry semantic meaning, with
Semantic Layer or Creating Views Forever?
Aren’t you tired of creating Views over and over again? How much time are you wasting managing Views? Can you keep track of all the Views in your databases? If any of the above relates to you or if you have concerns about the scalability of this approach, this blog
Timbr Powers the Ontology-based Semantic Data Fabric
Introduction Enterprises need flexible, scalable, and efficient ways to manage their ever-growing datasets. Traditional data architectures, such as centralized data lakes or warehouses, often struggle to meet the demands of modern organizations, particularly in terms of flexibility, governance, and real-time accessibility. Timbr intelligent semantic layer enables the Semantic Data Fabric
Building Better Data Applications: How Timbr Powers Flexibility, Performance, and Security
Developers of data applications often face challenges like complex queries, integrating multiple data sources, and ensuring high performance and real-time access while maintaining security and scalability. Timbr simplifies querying by replacing complex SQL JOINs with semantic relationships, enabling more efficient data access, integration across multiple sources without replication, and real-time
Data Relationships: Then and Now
A Comparison of SQL Relationships and Timbr’s Semantic Relationships Traditional SQL databases use primary and foreign keys mainly for data validation, rather than for managing relationships between tables. Foreign keys can’t express the nuanced meanings, roles, or inverse relationships that are crucial for understanding and querying interconnected data effectively, especially
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
