When knowledge graphs in your company get larger and larger, a scalable graph search is in high demand. In the current graph search solutions, scalability is still a big issue. Furthermore, with the fast development of deep learning on graphs, many companies rely on deep learning methods to mine insights from the ever-increasing knowledge graphs. But search and mining are usually not available in one package. This presentation will showcase the scalable solutions from Katana Graph which provide the end-to-end solutions for graph search and mining. It is ten times faster than the current market solutions and scales exponentially on graphs with billions or even trillions of nodes. It provides weighted k-shorted path searches and cutting edge graph deep learning methods (such as cluster-graph convolutional neural network, graph attention model, and graph transformer). Katana Graph is a start-up company founded by several faculty from University of Texas at Austin with the goal to provide the scalable graph search and deep graph mining in one click. In this presentation, we will showcase several use cases, such as searching and mining large scale knowledge graphs in drug discovery.