Powering AI with Real-time Data and Analytics
Kinetica is a real-time analytical database that ingests and analyzes high-velocity, multimodal data in a single engine, enabling fast SQL analytics over relational, time-series, graph, and vector data.
For healthcare organizations, this enables real-time analysis across clinical records, documents, imaging metadata, and genomic datasets without data movement.
The Reality of Healthcare Data
Healthcare data is diverse, fast-moving, and inherently interconnected. Clinical events, imaging, documents, genomics, and relationships arrive continuously and must be analyzed together to support safe and effective decisions.
Kinetica Database: Architecture Overview
Kinetica is designed for real-time analytics, enabling continuous data ingestion, distributed management, and fast parallel processing for immediate insights in a single unified engine.
Unified Multi‑Modal Analytics Engine
Kinetica unifies relational, time-series, geospatial, knowledge graph, vector, and full-text analytics, letting clinical data, documents, imaging metadata, and genomic datasets be queried together.
- Seamless analytics across diverse data types.
- High performance filters, joins across large, distributed datasets.
- Expressive analytics through a single SQL API
Consistent performance for complex ad-hoc and continuous queries on static and growing datasets.
Real‑Time Distributed Ingest
Kinetica continuously ingests data from multiple healthcare sources in real time, with distributed ingestion enabling analytics on live clinical data.
- Decoupled ingest and query execution
- Built for continuous insertion and update of datasets, not batch-oriented pipelines
- Built-in back-pressure handling for variable data rates in the native database stream engine.
Horizontally Scalable Distributed Compute
As data is ingested, it is automatically distributed across the cluster so each node processes its data locally. Query execution is pushed down to where data resides, enabling predictable scaling as data volume and query concurrency increase.
Innovative Data Store for Real-Time, Large and Continuously Growing Datasets
The data store is optimized for fast access to live healthcare datasets while maintaining access to older records without degrading real-time performance. This supports both interactive clinical queries and long-running analytical workloads.
Better Utilization of Many-Core Compute: CPU and GPU
Kinetica decomposes queries into parallel fragments that execute across CPU and GPU resources. Compute-intensive operations such as filtering, aggregation, and joins are optimized for vectorized execution.
Real‑Time Insights and Downstream Action
Kinetica enables real‑time insight directly from live healthcare data—without batch windows or offline pipelines. Results can be consumed immediately by dashboards, applications, visualization tools, and
AI‑driven workflows. Alerts can be triggered from complex predicates as data is ingested, ensuring timely awareness and action across clinical and operational use cases
Healthcare Data Types Unified in Kinetica
Clinical Records (FHIR, Structured Events)
Kinetica supports ingesting and querying patient, encounter, observation, medication, and condition data.Native JSON and relational support enables flexible modeling of longitudinal clinical histories.
Clinical Documents
Document metadata, extracted text, and embeddings can be stored and queried using full‑text and vector search, enabling fast retrieval and AI‑assisted workflows while preserving system‑of‑record integrity.
Medical Imaging Metadata
Genomics and Omics Data
Medical Knowledge Graphs and Clinical Ontologies
Kinetica provides advanced graph capabilities purpose‑built for large‑scale analytical medical knowledge graphs. The platform combines relational analytics, temporal graph processing, and vector search within a single engine.
Clinical entities are represented as tables, while relationships are stored as typed, time‑bounded edges. Medical ontologies and hierarchies can be modeled natively, enabling analytics that respect both structure and longitudinal clinical context.
Unique Capabilities
- Temporal knowledge graphs for disease progression and treatment pathways
- Population‑scale analytical graph queries
- Real‑time graph updates from streaming healthcare data
AI‑Ready Foundation for Healthcare
Kinetica provides a unified foundation for AI and retrieval‑augmented workflows in healthcare. The platform stores structured data, graph relationships, and vector embeddings together—enabling grounded, explainable context for AI systems.
Graph‑aware retrieval combined with semantic search and structured filters supports advanced use cases such as clinical decision support, intelligent assistants, research copilots, and care pathway optimization without requiring separate graph or vector databases.
Why Kinetica for Healthcare
Healthcare is not an add-on for Kinetica—it is a core industry the platform is built to serve.
Real-time
Designed for real-time, continuously evolving healthcare data
Unified
Unified multimodal analytics in a single platform
Predictable
Predictable performance for mission-critical environments
Scalable
Talk to Us!
The best way to appreciate the possibilities that Kinetica brings to high-performance real-time analytics is to see it in action.
Contact us, and we’ll give you a tour of Kinetica. We can also help you get started using it with your own data, your own schemas and your own queries.
