The latest update to Graph Data Science (GDS) introduces several significant features to make graph data science easier to use. The new algorithms include K-means clustering and Leiden for community detection, which provide more accurate and efficient results. Additionally, the update includes autotuning for machine learning pipelines, source and target node filtering for KNN and Node Similarity, and arrow support for fast graph projection, database creation, and graph export. These features significantly improve performance, allowing users to train models up to 10 times faster than before. The Graph Data Science Python Client also receives visual progress logging, which provides a better user experience. Furthermore, the update introduces new alpha-tier algorithms, including Leiden and K-means clustering, as well as node regression pipelines for predicting numerical property values. The integration of Apache Arrow enables users to import and export massive graphs directly into GDS at speeds up to 30 million objects/second. Overall, this update aims to make graph data science more accessible and user-friendly, empowering data scientists to work with connected data more efficiently.