Python In London with Memgraph
'Graph-based stream processing in Python'
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9 Mar 2022
Remove via Zoom
Memgraph is a streaming graph application platform that helps you wrangle your streaming data, build sophisticated models that you can query in real-time, and develop applications you never thought possible in days, not months.
In this talk Ivan and Katarina - two DevRel Engineers from Memgraph - will explore graph-based stream processing in Python and how this technology can be implemented into real-world solutions.
Before the talk, a guide will be sent out so attendees can install the necessary tools to follow along if desired.
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
Ivan Despot (Developer Relations Engineer, Memgraph)
Ivan is a Developer Relations Engineer at Memgraph. His passion for mathematics and graph theory inspired him to become part of the Memgraph team and start contributing to the field of graph analytics. Besides graph-based technologies, he is also interested in streaming platforms, stream processing and event-driven development.
Twitter: https://twitter.com/ivan_g_despot LinkedIn: https://www.linkedin.com/in/ivan-g-despot/ Medium: https://gdespot.medium.com/
Katarina Šupe (Developer Relations Engineer, Memgraph)
Katarina is a Developer Relations Engineer at Memgraph. Her journey there started with a summer internship, and her mathematics and computer science background was a perfect match to work in Memgraph. She enjoys contributing to different areas and exploring new real-time data visualization technologies. She sees the graph world as a future of data analytics due to the variety of algorithms used for stream processing.
Twitter: https://twitter.com/supe_katarina Linkedin: https://www.linkedin.com/in/katarina-supe/ Medium: https://email@example.com