A classic problem in unsupervised learning and data analysis is to find simpler and easy-to-visualize representations of the data that preserve its essential properties. A widely-used method to preserve the underlying hierarchical structure of the data while reducing its complexity is to find an embedding of the data into a tree or an ultrametric. The most popular algorithms for this task are the classic "linkage" algorithms (single, average, or complete). However, these methods exhibit a quite prohibitive running time of Θ(n^2). In this paper, we provide a new algorithm which takes as input a set of points P in R^d, and for every c> 1, runs in time n^1+O(1/c^2) to output an ultrametric Δ such that for any two points u,v in P, we have Δ(u,v) is within a multiplicative factor of 5c to the distance between u and v in the "best" ultrametric representation of P. Here, the best ultrametric is the ultrametric Δ^* that minimizes the maximum distance distortion with respect to the ℓ_2 distance, namely that minimizes max_u,v ∈ PΔ^*(u,v)/||u-v||_2." We complement the above result by showing that under popular complexity theoretic assumptions, for every constant ϵ>0, no algorithm with running time n^2-ϵ can distinguish between inputs that admit isometric embedding and inputs that can incur a distortion of 3/2 in L∞ -metric. Finally, we present empirical evaluation on classic machine learning datasets and show that the output of our algorithm is comparable to the output of the linkage algorithms while achieving a much faster running time.