Data Layer
This is the kind of data layer we work with using our information model and platform. Our approach adapts to your existing data landscape — whether you're using Snowflake, Databricks, Neo4j, PostgreSQL, MongoDB, or other common data platforms.
Working with Your Existing Data Platforms
Customers often use a mix of different data platforms for different purposes. We work with this reality in two ways:
Sometimes we fix this for you — We help integrate and unify your existing data platforms, creating a coherent data layer that works across your organization.
Sometimes we consume the data — We ingest data from your existing platforms into our data models, creating a unified view without requiring you to change your current infrastructure.
Digital Tvilling Information Model (DTIM)
The heart of our approach is the Digital Tvilling Information Model (DTIM) — a common language for describing key elements like locations, objects, events, and metrics. This model makes it possible to build a coherent view of how things are connected, what has happened, and how different pieces influence each other — all grounded in real-world data.
Instead of forcing all data into one format or location, DTIM connects distributed data while preserving its context, making it accessible, reusable, and meaningful across different applications and domains.
Supported Data Types
Graph data
For representing relationships, dependencies, and structures. Perfect for modeling complex systems, causal relationships, and organizational structures.
Time series
Optimized for high-volume, time-stamped data. Handles sensor data, metrics, and any data that changes over time with high performance.
Relational data
Structured, schema-based data for clear integration points. Maintains compatibility with existing database systems and provides familiar query interfaces.
Geospatial data
For mapping, routing, and location-based analysis. Supports complex geographic relationships and spatial queries.
Object storage
For unstructured files like documents, logs, or images. Provides flexible storage for any type of content while maintaining metadata and relationships.
Vector data
For AI/ML use cases like semantic search and clustering. Enables similarity searches and machine learning workflows.
Versioning
Native support for tracking changes and enabling reproducibility. Every change is recorded with full audit trails.
Key Benefits
- Works with your existing infrastructure: No need to replace current data platforms
- Unified access: All data types accessible through consistent interfaces regardless of source
- Preserved context: Data relationships and metadata are maintained across platforms
- Flexible integration: Adapts to your data landscape rather than forcing changes
- Scalable performance: Optimized for both real-time and analytical workloads
- Future-proof: Designed to handle new data types and platforms as they emerge