Many real-world data are naturally represented as tensors, or multi-dimensional arrays. Tensor decomposition is an important tool to analyze tensors for various applications such as latent concept discovery, trend analysis, clustering, and anomaly detection. However, existing tools for tensor analysis do not scale well for billion-scale tensors or offer limited functionalities. In this paper, we propose BlGtensor, a large-scale tensor mining library that tackles both of the above problems. Carefully designed for scalability, BlGtensor decomposes at least 100x larger tensors than the current state of the art. Furthermore, BlGtensor provides a variety of distributed tensor operations and tensor generation methods. We demonstrate how BlGtensor can help users discover hidden concepts and analyze trends from large-scale tensors that are hard to be processed by existing tensor tools.