A semantic layer of choice features unique capabilities.

The semantic layer is a cornerstone of efficient data management and utilization. It transforms raw data into a meaningful and business-friendly format, making it accessible for a wide range of users across an organization.

Timbr semantic layer stands out by seamlessly integrating into diverse data architectures implemented in Azure, Microsoft Fabric, OneLake, AWS Cloud, Google Cloud, Databricks, and Snowflake. This flexibility ensures that enterprises can leverage their existing infrastructure while enhancing data interoperability. Additionally, Timbr supports seamless integration with all SQL-fluent databases, providing a unified and consistent data modeling approach that empowers data engineers and analysts to work with data intuitively and efficiently.

Timbr semantic layer features the following capabilities:

1. Agile Data Modeling: Allows representation and use of data, providing a unified view of disparate data sources, by means of concepts with explicit relationships, business rules, hierarchies, and classifications, enabling easier and shorter no-JOINs SQL queries.
2. Virtual Layer Mapped to Data: Timbr’s virtual approach allows for fast development and deployment of data apps by eliminating the need for physical data movement and schema changes. This flexibility enables rapid adaptation to new data sources, evolving data models, and changing business requirements, reducing development cycles and time to market.
3. Data Virtualization: Timbr provides data virtualization with a powerful virtualization engine to join multiple data sources at scale, allowing data apps to access and integrate data from various sources and formats in real time. This logical abstraction layer enables seamless querying and consumption of data from multiple sources, simplifying data integration, reducing data redundancy, and improving agility in data app development.
4. Integration with BI Tools: Connects to all business intelligence apps (ODBC/JDBC), offering a single, shareable data model for all data consumers.
5. Visual Data Exploration: Enables visual discovery and exploration of data as a graph, understanding relationships and properties across data sources, and investigating data for informed decision-making.
6. Optimized Application Performance: Provides a unique REST API with Swagger for schema-based access, GraphQL-like data fetching, and reduced app complexity for application developers.
7. Support for ML Projects: Accelerates machine learning projects with access to the semantic model using SQL, R, Python, Scala, and Java, and through Python’s JPype, JayDeBe API, and SQL-Alchemy libraries.
8. Data Security and Governance: Timbr offers robust security and governance features, including fine-grained access controls, data masking, data encryption, data lineage, and auditing capabilities. These features ensure data confidentiality, integrity, and compliance, helping organizations maintain data privacy, meet regulatory requirements, and establish trust in their data apps.
9. Performance Optimization: Timbr optimizes data access and query performance through query optimization, caching, and intelligent query routing. These techniques enhance the responsiveness and scalability of data apps, ensuring efficient handling of high volumes of data and user requests.
10. Cost Efficiency: Timbr’s data virtualization capabilities reduce infrastructure and operational costs associated with data integration by minimizing the need for data replication, storage, and movement. This results in cost savings in hardware, storage, and maintenance, while its agility and efficiency contribute to lower development and maintenance costs for data apps.