CoSupport AI
What is Aerospike?
Aerospike is engineered as a distributed NoSQL database tailored for real-time applications demanding high throughput and exceptionally low latency. It consistently delivers read and write operations in under a millisecond, enabling organizations to manage vast data volumes with remarkable efficiency. The platform's architecture supports infinite scalability, allowing systems to expand seamlessly from gigabytes to petabytes without sacrificing performance or predictability.
Beyond raw speed and scale, Aerospike focuses on operational efficiency and cost reduction, potentially lowering server requirements by up to 80% compared to traditional solutions, which also aids in meeting sustainability targets. It provides versatile data model support, encompassing graph databases compatible with Apache TinkerPop and Gremlin, flexible JSON document storage, advanced vector search capabilities crucial for AI-driven similarity insights, and a high-performance key-value store. This multi-model flexibility addresses a wide spectrum of application needs, ranging from efficient caching and session management to complex real-time analytics and powering sophisticated AI applications.
Features
- Sub-Millisecond Latency: Achieves extremely fast read and write times.
- Infinite Scalability: Scales from gigabytes to petabytes without performance degradation.
- High Availability: Ensures systems are always operational globally.
- Reduced Infrastructure Costs: Operates efficiently with significantly fewer servers.
- Multi-Model Database: Supports Graph, JSON Document, Vector Search, and Key-Value data models.
- Hybrid Memory Architecture™: Utilizes patented technology for optimized speed.
- AI-Ready Data Handling: Efficiently ingests massive streaming data for AI applications.
- Vector Search Capability: Develops applications drawing similarity-based insights in real-time.
Use Cases
- Real-time analytics platforms
- High-speed caching layers
- User session management
- AI/ML data ingestion and processing
- Real-time vector similarity search
- Applications requiring graph data analysis
- High-throughput transactional systems
- Large-scale data storage and retrieval
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