TigerGraph
TigerGraph is a native parallel graph database platform designed for real-time analytics on complex, interconnected data. It uses a distributed, in-memory architecture to handle large-scale graph queries efficiently, supporting both transactional and analytical workloads. It is particularly known for its ability to perform deep link analysis and pattern matching across billions of entities and relationships.
Developers should learn TigerGraph when working on applications that require advanced graph analytics, such as fraud detection, recommendation engines, network analysis, or social network insights, where relationships between data points are critical. It is ideal for use cases involving real-time queries on highly connected data, such as in finance, healthcare, or IoT, due to its scalability and performance with complex traversals.