Over the past decade, graph databases have become an indispensable asset in dealing with networked and non-relational data. However, as the amount of data ingested into graph databases has exploded, performance has become a key criterion when determining which one to use. In addition, it is critical for organizations to understand the type of use cases in which graph databases can add business value relative to more traditional SQL or other types of NoSQL databases. During the workshop, we will dig into the fundamentals of Labeled Property Graphs and highlight use cases, ranging from fraud detection, money laundering, and complex manufacturing to real-time analytics for Customer 360. We will show where graph databases are not simply an option but perhaps the only choice available. Lastly, we will explore how to use Python to interact with the graph databases to extract features to be used in ML models. This hands-on workshop will cover: - Graph Fundamentals - Graph Use-Cases - Introduction to TigerGraph Cloud - Integrating Python with TigerGraph Cloud - Feature Generation for Supervised Machine Learning