Graph-Based Stream Processing in Python - Ivan Despot
Graph-Based Stream Processing in Python - Ivan Despot
Nowadays, 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 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. Those analytics can calculate the PageRank, identify communities, traverse relationships, etc. While solutions such as ksqlDB or Apache Spark help process 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 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 implementing 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.
We are going to showcase the Twitter Network Analysis demo, a web application with a Python Flask backend that uses Memgraph to ingest real-time data scraped from Twitter. You will learn how to use an Object Graph Mapper (OGM) in Python to connect to a graph database and perform stream processing on data streamed with Apache Kafka.
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