In today’s data-driven world, processing speed and instant insights matter more than ever. The GridGain data platform is purpose-built to power real-time, highly scalable transactional, analytical, and AI applications — delivering consistent processing and contextual intelligence that modern businesses demand, all at low-millisecond latencies and massive scale.
The distributed, in-memory multi-model data store is one of its platform's foundational pillars, which enables real-time storage and processing across several types of applications throughout an enterprise. GridGain’s architecture supports diverse data types: relational, columnar, key-value, document, geospatial, vector, and unstructured. The highly scalable millisecond latency data store, colocated compute, and several other key capabilities make it the ideal solution for real-time transactional, analytical, and AI applications. Read on to learn how GridGain uniquely powers these workloads.
Real-Time Transactional Applications
GridGain supports ANSI-SQL and fully ACID-compliant transactions across distributed nodes. It also goes one step further and provides configurable strict or eventual consistency, with the flexibility to determine the consistency level per data cache. For example, strict consistency is critical for financial transactions such as updating bank balances or confirming stock trades - instantly and accurately. On the other hand, the eventual consistency feature can be used when updating a shopping cart — it’s okay if updates sync across devices a few seconds later. Or showing social media likes or comments — the exact number isn’t critical in real time. With GridGain, enterprises can appropriately configure their cluster for specific use cases.
GridGain also includes support for stream processing, continuous queries, fast ingestion APIs, and durable persistence with snapshots and point-in-time recovery. These capabilities, together with its distributed multi-model data store and colocated compute, make it an ideal platform for high-volume, low-latency transactional applications.
Real-Time Analytics and Streaming Insights
With built-in Hybrid Transactional / Analytical Processing (HTAP), GridGain enables simultaneous OLTP and OLAP processing, eliminating the need for ETL pipelines. Complex joins and queries across row and columnar formats (federated queries) are also handled efficiently in the latest product release. Such capability allows you to easily join live user actions (such as clicks and purchases) stored in row format with historical behavior or demographics stored in columnar format to deliver real-time personalized recommendations or marketing triggers.
GridGain also provides built-in support for colocated and segregated deployment models. In a colocated model, row and columnar stores reside on the same physical nodes, which helps with lower replication lag, but also may result in resource contention between OLTP and OLAP ("noisy neighbor" effect). For example, e-commerce monitoring on live transactional data or credit risk scoring during a loan application are the right use cases for a colocated deployment model. In a segregated model, the row and columnar stores reside on separate nodes, providing greater fault isolation and performance tuning flexibility, as well as a separation of transactional and analytical workloads to enable analytics-intensive use cases without impacting transactional performance (eliminating the noisy neighbor effect). This is the right model for use cases that require heavy analytical workloads on large transactional datasets, or when you want to ensure analytical processes don’t interfere with real-time systems, such as in regulated industries.
In addition, GridGain’s integrations with Kafka, Iceberg, Parquet and JSON enable enterprises to deliver highly performant real-time analysis across complex data workloads. Together with its signature multi-model data store with colocated compute, these capabilities make it the best platform for real-time analytics applications such as fraud detection, real-time dashboards, dynamic pricing, IoT-based failure alerts, and other high-stakes operational analytics.
AI and GenAI Acceleration
GridGain provides native support for real-time AI workloads with capabilities such as an online feature store to enable real-time feature extraction from streaming or transactional data, and prediction cache serving for pre-computed predictions, or running predictive models on demand. It also provides model store capabilities for ML apps. Such capabilities make it an ideal solution for predictive inference applications like recommendation engines.
For GenAI, GridGain provides a robust foundation for real-time Retrieval-Augmented Generation (RAG) applications. These applications often rely on fast retrieval of up-to-date information to ground LLM responses. GridGain supports vector storage and similarity search, allowing you to store and retrieve embeddings efficiently, which are key for LLM-based search, semantic retrieval, and content recommendation. It also supports SQL retrieval to incorporate additional context in LLM responses. GenAI systems often need to process events, logs, documents, or APIs in real time. GridGain can ingest JSON, Kafka streams, and more with low latency. As GenAI apps scale (e.g., chatbot traffic, retrieval queries), GridGain scales easily with its distributed architecture and in-memory processing, as well as colocated compute. In addition, it offers integrations with open-source tools and libraries, such as Feast, LangChain, and LangFlow.
These capabilities together are why GridGain is the only unified real-time data storage and processing platform for high volume, ultra-low latency AI, transactional, and analytical workloads.