Over the last decade, many enterprises have made great strides in achieving the real-time processing of massive amounts of data. Today, however, generative AI (GenAI) and agentic AI applications require even more data from more sources, along with context engineering, dynamic data interactions, and more intensive data analysis – placing even greater demands on the data infrastructure.
To create AI-powered applications for customer and employee-facing use cases, companies must create a reliable foundation for secure, efficient, low-latency data processing at scale. This crucial requirement is best accomplished with hybrid transactional/analytical processing (HTAP), which enables high-speed transactional lookups (OLTP) on the latest operational or streaming data while concurrently executing complex analytical queries (OLAP) on historical data.
HTAP is based on a centralized, highly scalable, memory-first storage layer that automatically pulls relevant subsets of data from diverse and distributed sources, including on-prem and cloud-based databases and data warehouses, transactional systems, and streaming data sources. The aggregated data in the HTAP in-memory cache can then be instantly processed at scale. HTAP eliminates the performance impact of applications that would otherwise make direct calls to the source systems for each query and significantly reduces the need to move data across the network.
Here I’ll explore some of the data challenges that today’s new AI applications present and how HTAP solves them.
HTAP for Four Common GenAI Data Challenges
Real-time RAG (Retrieval-Augmented Generation) – GenAI models can "hallucinate" or provide outdated information. RAG solves this by incorporating relevant, up-to-date information from reliable external knowledge bases and enterprise systems. HTAP directly enables the real-time integration of this external data into the AI model processing flow.
Dynamic Context and Personalization – Many GenAI applications in customer service, e-commerce, and content creation require personalized responses based on a user's current activity (streaming), preferences (historical), and latest transactional data (e.g., current inventory). HTAP enables simultaneous analysis of all this data to generate highly personalized recommendations or relevant content on-the-fly.
Real-time Feature Engineering – GenAI applications often require “features” – the data points used by the model – to be extracted or generated in real-time from streaming or transactional data. The design of HTAP enables it to support online feature stores for real-time feature extraction and vector embedding generation from live data streams or ongoing transactions, making data instantly available to AI applications.
Prediction Caching and Model Serving – GenAI applications may leverage pre-computed predictions or run smaller predictive models on demand to inform its content generation process. HTAP enables efficient serving of these predictions from a prediction cache and supports model store capabilities for rapid access and execution of predictive models.
HTAP for Four Common Agentic AI Data Challenges
Autonomous Decision-Making and Action – To enable Agentic AI systems to make decisions and take actions with little or no human intervention, agents must instantly react to real-time events (e.g., dynamic pricing, IoT failure alerts) based on the freshest operational data. HTAP provides the ability to process high-volume transactional data while simultaneously running complex analytical models or rules against that data to identify anomalies or trigger actions in sub-second timeframes.
Continuous Learning and Adaptability – For Agentic AI systems to evolve based on new information and outcomes, they must rely on real-time feedback loops where the agent takes an action, observes the result, and updates its internal state or behavior. HTAP enables the seamless integration of these “transactional” updates with the analytical processing required for model retraining or adaptation.
Complex, Multi-step Workflows – Agents frequently coordinate and execute multiple tasks simultaneously, pulling data from various internal and external sources, and adapting process flows on-the-fly. With HTAP, agentic systems have real-time access to fragmented data across silos. Agents can ingest this data in diverse formats (JSON, Kafka streams, etc.) and perform federated queries across live and historical datasets to ensure sufficient context for making complex decisions.
Proactive Monitoring – To enable agents to monitor and respond to disruptions in real-time, HTAP enables the creation of real-time dashboards and alerting based on huge amounts of live transactional data.
How GridGain Delivers Low-Latency HTAP & AI
HTAP is built into GridGain, unifying OLTP and OLAP workloads on a single, high-performance, memory-first platform that also supports a persistence layer. GridGain efficiently handles complex joins and queries across row and columnar formats (federated queries), making it easy, for example, to join live user actions stored in a row format with historical behavior or demographics stored in a columnar format.
GridGain’s support for a segregated deployment model enables row and columnar stores to be on separate nodes. Updates from the transactional store are streamed asynchronously to the analytical store in the background, ensuring transactions are never delayed, even when analytical workloads are heavy.
GridGain includes a unified SQL engine that intelligently routes queries to the optimal location – rows for transactions or columns for analytics – automatically leveraging columnar optimizations for analytical workloads.
GridGain provides native support for real-time AI workloads. A built-in online feature store enables real-time feature extraction from streaming or transactional data, and a prediction cache serves pre-computed predictions or runs predictive models on demand. GridGain also provides model store capabilities for ML apps and performs feature extraction and generates vector embeddings from transactions in real-time to optimize AI and RAG applications.
GridGain supports vector storage and similarity search for efficient embedding storage and retrieval. In addition, GridGain offers integrations with open-source tools and libraries, such as Feast, LangChain, and LangFlow.
GridGain: The Foundation for AI Success
To meet their strategic and competitive goals, businesses must support real-time GenAI and agentic AI applications. Low-latency HTAP is essential to making this happen. GridGain was designed to deliver low-latency HTAP, and it now provides native support for AI workloads.
To learn how GridGain can transform your data infrastructure and accelerate your AI transformation, book a technical consult.