Use Case
Supercharge
Data Lakes with Intelligent Semantic Relationships
Data lakes offer flexibility and scalability, but the diverse range of stored data formats often complicates insight extraction. The lack of structured relationships, data inconsistencies, and the need for complex queries slow down analysis and hinder decision-making.
Timbr’s intelligent ontology-based semantic layer transforms raw data lakes into interconnected, queryable repositories of knowledge. By introducing semantic modeling and explicit relationships that simplify queries, Timbr unlocks the full analytical potential of data lakes of the major platforms.
BEHIND THE USE
Timbr introduces an intelligent framework that organizes and adds meaning to the data within data lakes, enabling more powerful and efficient querying.
REEFERENCE ARCHITECTURES
Timbr embedds semantic intelligence into the leading platforms: Databricks Delta Lake, Amazon S3, Azure Data Lake Storage, Google Data Lakehouse and Snowflake.
Challenges
Is Your Data Lake Lacking Structure?
Data lakes excel in flexibility but lack enforced schemas and relationships, leaving data disconnected and difficult to navigate. Without a clear structure, organizations struggle to derive value, resulting in time-intensive data wrangling and inconsistent insights.
Struggling with Complex Queries Across Diverse Data Types?Data lakes, including popular platforms like Databricks Delta Lake, store unstructured, semi-structured, and structured data. Querying across these formats can be cumbersome, requiring complex SQL queries and manual joins. Timbr simplifies this challenge by introducing a semantic layer that reduces query complexity, regardless of the underlying data lake.
Delayed Insights Due to Schema-on-Read?
With a schema-on-read approach, data structuring happens only at the time of analysis. This delays insights and adds overhead to data exploration, creating bottlenecks for teams attempting to access meaningful insights in real-time.
Why Timbr
Enforcing Semantic Structure Without Changing the Data
Timbr’s intelligent semantic layer works seamlessly with all major data lakes, including Databricks Delta Lake. It overlays a logical structure, introducing explicit relationships and context to your data without altering its physical storage. For Databricks users, this means leveraging Timbr’s semantic models directly within Databricks Notebooks and Unity Catalog for enhanced accessibility and governance.
Simplified Querying Across All Data Types
Timbr ensures queries across any data lake become straightforward and SQL-based. Analysts can write intuitive queries on complex data without needing to worry about joins or inconsistencies—whether the data is in Azure Data Lake Storage, Amazon S3, or Databricks.
Streamlined Data Exploration and Discovery
With Timbr, users can accelerate data exploration using meaningful semantic relationships. Analysts can explore data in Delta Lake or other storage solutions more intuitively, transforming raw data into actionable business insights faster.
Impact
Faster, Smarter Insights
Timbr empowers analysts to query data faster across all data lakes, including structured, semi-structured, and unstructured data sources. For organizations leveraging Databricks Delta Lake, Timbr integrates natively with Databricks Notebooks, enabling direct querying in SQL, Python, R, or Scala for rapid decision-making.
Enhanced Accessibility and Collaboration
Whether you’re using Databricks, Azure, or Amazon S3, Timbr introduces semantic relationships that make data lakes more accessible to all users. With integrations into platforms like Unity Catalog, business users and analysts can collaborate seamlessly using consistent and governed data definitions.
Cost Efficiency with Scalable Analysis
By eliminating the need for costly data transformations and reducing query complexities, Timbr ensures scalable and cost-effective analysis of even the largest data lakes. Organizations can maximize their data lakes’ value without additional infrastructure investments.
