This week we used a subset of a published NFT Trades dataset to model, import, and analyze in Neo4j AuraDB Free. We started with a tweet about research on NFT trades, which led us to the authors' data, a 6.1M trades CSV file. After importing the data into Neo4j AuraDB Free, we created a graph model with nodes for NFTs, traders, and transactions, and relationships between them. We then analyzed the data using queries such as counting the number of trades, finding the top 10% of traders who account for 85% of transactions, and identifying the highest profit made by a trader on a given day. We also used Neo4j Bloom to visualize the data, exploring co-buying behavior, collections of NFTs, and transaction volume and cryptocurrency. The analysis revealed interesting insights into the NFT market, including pure sellers, buyers, and traders who trade frequently with each other. This was just a small taste of what can be done with this data, and we encourage readers to explore further and come up with their own queries and visualizations.