Vector databases are more suitable for tasks involving similarity and machine learning, while knowledge graph databases excel in modeling and querying interconnected, complex, semantically rich data.

Since the launch of Neptune in 2018, it has become one of the leading services for storing graph data and performing updates and election on specific subside of the graph. However, one of the ...

A graph database is different from a traditional relational database in how it is structured. Instead of using rows and tables to organize data, a graph database has nodes and edges to build out a ...

Every property we measure in a vector embedding constitutes a dimension of the graph, resulting in it usually having many more than the three dimensions we can conventionally visualize.

What is GraphRAG? GraphRAG is a retrieval method that uses knowledge graphs to store and manage structured data, serving as an alternative to VectorRAG (Vector Retrieval Augmented Generation ...

These include updates to its Spanner SQL database, which now features graph and vector search support, as well as extended full-text search capabilities. This wouldn’t be a Google announcement ...

Google’s June 2025 Core Update just finished. What’s notable is that while some say it was a big update, it didn’t feel ...

Data types. Vector embeddings were the rookies of the year in 2024 as new ... “It’s basically the same architecture as RAG with vectors but with a knowledge graph layered into the ...

Available starting today, Neo4j Aura Graph Analytics is said to work with any kind of data source, including Oracle, Microsoft SQL, Databricks, Google BigQuery, Snowflake and Microsoft OneLake.