UNIFIED BACK-END SYSTEMS
There has been a movement afoot to bring databases, caches, and applications into one unified system. “For some businesses, like ecommerce, the speed at which personalized product pages can be delivered has such a major impact on conversion rates, and thus revenue, that many retailers are investing millions or tens of millions in high-performance unified back-end systems to meet their data delivery needs,” said Jaxon Repp, sales CTO and chief evangelist at Harper. “These systems get rid of lots of that extra computation and integration effort that goes into managing separate database, cache, and application systems.”
Potential Issues
Moving to a more unified backend requires thinking differently about data architecture, Repp advised. “Most developers are blind to the amount of system resources that are consumed on networking and intermediary serialization processes that happen between data and application systems.” Such unified technologies “are an entirely different breed of technology, and the playbooks for managing systems with it are still in their first editions.”
Tangible Business Benefits
Having a more unified back-end system that incorporates all the elements of data environments delivers “performance, performance, performance, and simplicity,” said Repp. “Light and electricity can only transmit information so fast, and the greater distance systems need to traverse to communicate, or the more processes between the request and information delivery, the slower the system will be. Unified systems collapse all the processes into the least computational and network-intensive form and place those systems as close to the user as reasonably possible.”
SUPER-INTELLIGENT DATA SYSTEMS
Super-intelligent data systemss may be on the rise, as the boundaries between today’s tools for managing AI and other data-intensive applications—“databases, knowledge graphs, analytics engines, and AI agents—are blurring,” said Rahul Rastogi, chief innovation officer at SingleStore. Be on the lookout for platforms “that seamlessly integrate vector intelligence with structured logic and blend contextual understanding with rule-based filtering.”
Then, a generation of data platforms will “empower users to not just access data, but collaborate with it,” Rastogi continued. “They won’t just store data—they’ll be AI-augmented to understand it, reason over it, and interact via natural language. We’re entering an era where semantic models, LLM [large language model]-powered reasoning, and natural language interfaces are converging to redefine what data systems can do.”
Potential Issues
Bringing all the pieces of a super-intelligent data platform will take time and patience, Rastogi cautioned. “The core technologies—semantic models, vector-native retrieval, LLM reasoning, real-time graph construction, and conversational interfaces—are all still maturing, each at a different pace. No single data platform delivers the full stack end-to-end yet.”
Tangible Business Benefits
“By consolidating semantic search, structured querying, governance, and AI reasoning into one platform, it reduces tech sprawl, cuts infrastructure and licensing costs, and streamlines data engineering,” said Rastogi.
Such super-intelligent data systems “will dynamically generate embeddings, construct relationship graphs, and surface insights proactively and in real time, at scale, across cloud-native, event-driven architectures,” Rastogi asserted. These systems will power “real-time, context-aware intelligence for applications like chatbots, personalization, and decision support.”