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.
A distributed computing paradigm that eliminates the central database bottleneck by co-locating data and processing logic in the same memory space.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
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.
Legacy databases are too slow to run complex fraud models during the transaction window.
Registry Updated:2/7/2026
Price updates take minutes to propagate across platforms, leading to lost revenue.
LLMs lack access to the most recent enterprise data updates.