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Real-time AI-powered data fabric for millisecond-latency enterprise applications.

GigaSpaces Smart DIH (Digital Integration Hub) is a high-performance Enterprise Data Grid (EDG) engineered for the 2026 AI-driven landscape. It leverages an in-memory data fabric architecture to decouple digital applications from legacy systems of record (SoR). By utilizing a Space-Based Architecture (SBA), it achieves sub-millisecond latency for complex query processing and AI model inference. The platform is specifically designed to handle the high-throughput requirements of Real-time RAG (Retrieval-Augmented Generation) and feature stores for machine learning. Its 2026 positioning focuses on the 'Total Data Awareness' paradigm, allowing enterprises to ingest, transform, and serve data across hybrid-cloud environments with linear scalability. The system integrates advanced Change Data Capture (CDC) to ensure data freshnees and features a 'Tiered Storage' engine that intelligently moves data between RAM, NVMe, and object storage based on access patterns, optimizing the cost-to-performance ratio for massive AI datasets.
GigaSpaces Smart DIH (Digital Integration Hub) is a high-performance Enterprise Data Grid (EDG) engineered for the 2026 AI-driven landscape.
Explore all tools that specialize in real-time feature engineering. This domain focus ensures GigaSpaces Smart DIH delivers optimized results for this specific requirement.
A distributed computing paradigm that eliminates the central database bottleneck by co-locating data and processing logic in the same memory space.
Automatically moves data between RAM, SSD (NVMe), and S3 based on access frequency and business rules.
Direct integration with Python-based models to execute inference inside the data grid without network hops.
Visual interface for mapping source data to the in-memory space with transformation logic.
Full ACID compliance across distributed data partitions.
WAN replication that allows for globally distributed data synchronization.
Full orchestration via K8s for automated scaling, self-healing, and rolling updates.
Deploy GigaSpaces cluster on Kubernetes (EKS, GKE, or AKS) using Helm charts.
Initialize the Space (In-Memory Data Grid) with defined partitions and replication factors.
Configure CDC (Change Data Capture) connectors to link legacy RDBMS or NoSQL sources.
Define the Data Schema using POJOs or Document-style templates.
Set up Tiered Storage policies for RAM-to-SSD overflow management.
Deploy Microservices using the GigaSpaces Processing Unit (PU) framework.
Configure the AI Inference engine to run models locally within the data grid memory space.
Implement Low-Latency Query API via the GigaSpaces Query Language (GS-QL).
Enable multi-region replication (WAN Gateway) for global data consistency.
Launch the Ops Manager dashboard for real-time monitoring and resource scaling.
All Set
Ready to go
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