Graph database is a data management system centered on graph structures, using nodes, edges, and properties to represent and store data, with a strong emphasis on describing relationships between data entities.

Distributed database is a database system in which data logically belongs to a single database but is physically stored across multiple computing nodes.

LLMOps (Large Language Model Operations) refers to a comprehensive set of methodologies and operational practices that manage the entire lifecycle of large language models (LLMs), including data preparation, model training, deployment, monitoring, and continuous optimization.

Currently, some graph databases offer native graph analytics capabilities, eliminating the need to export data to external computing platforms.

Multimodal large language models are powerful artificial intelligence models capable of processing diverse types of data input such as text, images, and audio, while generating natural language output.

With the in-depth development of the digital economy, traditional database and storage systems have gradually revealed certain limitations.

An enterprise-level data warehouse joint disaster recovery solution typically involves strategies and technologies across multiple dimensions to ensure data security, availability, and business continuity.

The big data foundation is a basic data platform that provides data storage, processing and analysis.

Against the backdrop of Industry 4.0 and intelligent manufacturing, industrial big data has become a critical tool for us to tap into the hidden value of production and manufacturing.

The application of industrial data should not pursue scale and comprehensiveness. Instead, enterprises should address weak links in the data chain—including data collection, parsing, storage, computing, and analysis—based on their actual conditions.