By ingesting data with Apache Kafka and performing graph-based stream processing in real-time, Memgraph is able to perform near-instantaneous graph analytics on vast amounts of data.

Memgraph is a streaming graph application platform that helps you wrangle your streaming data and build sophisticated models that you can query in real-time. Powered by an in-memory graph database and dynamic graph algorithms such as online PageRank, online community detection and an online node2vec embedding algorithm, Memgraph is able to avoid unnecessary recalculations and blockages in data analysis pipelines.

Graph analytics can provide insights into complex networks that would otherwise require resource-intensive computations. It is also much simpler to store streaming data in the form of graphs, as the NoSQL graph database approach doesn't rely on predefined and rigid tables. When connecting a Kafka data stream to Memgraph, you only need to create a transformation module that will map incoming messages to the property graph model. This data can then be traversed and analyzed using the Cypher query language without having to implement custom algorithms or relying on development-heavy solutions. MAGE (Memgraph Advanced Graph Extensions) is a graph library that works well with Kafka-powered systems and contains graph algorithms meant for analyzing streaming data. Besides stream processing, you can also utilize standard graph algorithms from the MAGE library to explore the stored data.