ontologies for AI & ML
Python
Timbr makes the data behind Python notebooks not just accessible, but intelligent.
Instead of wrangling tables and writing complex SQL joins, users query meaningful concepts, explore governed data models, and deliver insights faster, directly from familiar Python environments like PySpark, Pandas, Jupyter and TensorFlow.
Timbr Solves Data Teams' Challenges
Working with enterprise data from Python notebooks is often harder than it should be. Timbr addresses the most common pain points:
| Challenge | How Timbr Helps |
|---|---|
| Complex SQL queries with many JOINs | Replaces JOINs and UNIONs with semantic relationships to reduce query complexity |
| Difficulty in connecting to various data sources | Virtualizes sources semantically for fast, convenient access |
| Maintaining consistency and a single source of truth | Centralizes business logic and data definitions, ensuring reusability |
| Performance issues with large-scale data analysis | Generates optimized queries for the underlying databases and provides 4-tier caching to optimize performance |
| Delays from governance and access bottlenecks | Timbr enforces policies while enabling self-service semantic access |
| Lack of inherent semantic context in raw data structures | Virtual data models with business concepts, semantic relationships, and explicit context |
| Cleaning and preparing data for AI and advanced analytics | Makes data AI-ready with ontologies, rfelationships and context |
| Dealing with siloed in-app modeling or transformations | Enables data discovery and sharing, reducing time-to-value for data projects |
| Evolving data landscape disrupting existing mappings | Ontology can be extended without disrupting mappings, future-proofing data strategy |
| Time-consuming and complicated task of making sense of big data | Simplifies data discovery, maintains business context, eliminates technical difficulties |
Accelerated Data Exploration
Timbr ontologies turn complex data into intuitive, business-aligned concepts that are easy to query. Python users benefit from:
- Simplified Access: Simplified access to structured, context-rich data.
- Automatic Understanding: Automatic understanding of relationships and hierarchies.
- Governed Metrics: Governed metrics and filters applied automatically.
- Faster Exploration: Faster exploration with fewer errors and less guesswork.
Governed Access
- Row- and Column-Level Security: Row- and column-level security is enforced through the ontology.
- Lineage Tracking: Lineage tracking shows exactly how results are derived.
- Compliance and Consistency: Compliance and consistency are maintained automatically.
- Agile Notebooks: Notebooks remain agile without compromising governance.
