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When: 5 April 2022 at 16:00 (UTC) -
Speaker(s): Ivan Despot, Katarina Supe -
Talk(s): Graph-based stream processing with Apache Kafka®
Resources
[[Slides]]
The understanding of complex relationships and interdependencies between different data points is crucial to many decision-making processes.
Graph analytics have found their way into every major industry, from marketing and financial services to transportation. Fraud detection, recommendation engines, and process optimization are some of the use cases where real-time decisions are mission-critical, and the underlying domain can be easily modeled as a graph.
By ingesting data with Apache Kafka and applying graph-based stream processing in real-time, you can perform near-instantaneous graph analytics on vast amounts of data. When it comes to complex networks, it’s often necessary to perform graph algorithms such as calculating the PageRank, identifying communities, traversing relationships, etc. While solutions such as ksqlDB or Apache Spark are useful for processing relational data, Memgraph is an open-source streaming platform that can be used to analyze graph-based data models.
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 graph model 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.
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