Graph Databases Gaining Enterprise-Ready Features
Graph database vendors are broadening their applications by adding enterprise-focused features to help customers who are dealing with the burdens of huge troves of business-critical data.
In a move that is highlighting the growing maturity of the graph database marketplace, Neo4j recently unveiled its latest product, Neo4j for Graph Data Science, which is designed to make it easier for enterprises to use graph machine learning to expand their capabilities. Another vendor, Katana Graph, recently announced a collaboration with Intel to port and optimize its Katana Graph engine on Intel Xeon scalable processors, Xeon-based clusters and on Intel’s upcoming discrete GPUs.
Meanwhile, TigerGraph, recently unveiled the results of a new graph data management benchmark study that uses nearly 5TB of raw data on a cluster of machines to show the performance benefits enterprises can potentially receive using its graph database.
Graph databases are purpose-built to store and navigate what are called data “relationships,” according to documentation from Amazon Web Services. Relationships in graph databases are critical to bring disparate data together, using “nodes” to store data entities, and “edges” to store relationships between entities. Edges in graph databases include a start node, an end node, a type, and a direction. An edge can describe parent-child relationships, actions, ownership, and more. The number and kind of relationships a node in a graph database can have is unlimited.
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