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The complexity of modern data solutions goes far beyond the capabilities of modern relational databases. Some real-world relationships like social networks, blockchain transactions, network perimeters, and others are challenging when you try to model them within a relational or document database. But those problems are manageable if you solve them using graphs, nodes, and edges.<br><br>Neo4j is a popular and widely used graph database management system designed for storing, managing, and querying data using the principles of graph theory. It is designed to handle data that has complex relationships and connections, which may not be efficiently represented in a traditional relational model. Moreover, it has a fantastic, straightforward integration with Python, making it usable for solving data science, analytics, networking, etc.<br><br>In the following session, I will share my experience using the Neo4j database in the cloud computing space. I will cover how to analyze and visualize network relationships using Cypher, the Neo4j query language, PyVis, and NetworkX, the Python graph libraries. By the end of the presentation, the audience should understand the basic graph theory and how to apply it to solve their problems using Neo4j and Python.
