Graph-Based Stream Processing | Memgraph

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.

Join us for this special workshop on graph analytics with speakers from Memgaph, Ivan Despot and Katarina Šupe!

Memgraph is the platform for graph computation on streaming data. It's an end-to-end platform designed to solve complex graph problems in real-time and accelerate graph application development.

Learn more about Memgraph:
https://memgraph.com/

Topics:
00:00:00 Agenda and Speakers Introduction
00:03:38 The Graph Data Model
00:10:29 Jupyter Notebook Demo
00:13:59 Graph Analytics in Action
00:45:12 Graph Stream Processing
01:01:43 Graph Stream Processing with Memgraph
01:08:12 Further Discussion and Q&A

Highlights:
Analytics with Graph Data Structures
Insights into Complex Networks
Graph-Based Stream Processing
Getting Started with Memgraph

Speaker Bio:

::: Ivan Despot
Developer Relations Engineer, Memgraph

Ivan Despot 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:   / ivan_g_despot  
LinkedIn:   / ivan-g-despot  
Medium:   / gdespot  

::: Katarina Šupe
Developer Relations Engineer, Memgraph

Katarina Šupe 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:   / supe_katarina  
Linkedin:   / katarina-supe  
Medium:   / supe.katarina  

Hosted By

::: Dany Entezari
Managing Director, BigNumber

Computational software engineer, instructor, and founder of BigNumber. Dany has trained hundreds of industry professionals and students in programming, computer science, mathematics, and statistics.

Complete Talk Description

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.

Through this workshop, we would cover theoretical topics such as graph data structures, the property graph model, various traversal techniques and graph algorithms. We would demonstrate graph analytics on specific use cases, from performing social network analysis to real-time fraud detection.

Please join the BigNumber Slack channel for further information and discussions, https://join.slack.com/t/bignumber/sh...

Website:
https://www.bignumber.xyz