As it has become easier and cheaper to collect big datasets in the last few decades, designing efficient and low-cost algorithms for these datasets has attracted unprecedented attention. However, in most applications, even storing datasets as acquired has become extremely costly and inefficient, which motivates the study of sublinear algorithms. This thesis focuses on studying two fundamental graph problems in the sublinear regime. Furthermore, it presents a fast kernel density estimation algorithm and data structure. The first part of this thesis focuses on graph spectral sparsification in dynamic streams. Our algorithm achieves almost optimal runtime and space simultaneously in a single pass. Our method is based on a novel bucketing scheme that enables us to recover high effective resistance edges faster. This contribution presents a novel approach to the effective resistance embedding of the graph, using locality-sensitive hash functions, with possible further future applications. The second part of this thesis presents spanner construction results in the dynamic streams and the simultaneous communication models. First, we show how one can construct a $\tilde{O}(n^{2/3})$-spanner using the above-mentioned almost-optimal single-pass spectral sparsifier, resulting in the first single-pass algorithm for non-trivial spanner construction in the literature. Then, we generalize this result to constructing $\tilde{O}(n^{2/3(1-\alpha)})$-spanners using $\tilde{O}(n^{1+\alpha})$ space for any $\alpha \in [0,1]$, providing a smooth trade-off between distortion and memory complexity. Moreover, we study the simultaneous communication model and propose a novel protocol with low per player information. Also, we show how one can leverage more rounds of communication in this setting to achieve better distortion guarantees. Finally, in the third part of this thesis, we study the kernel density estimation problem. In this problem, given a kernel function, an input dataset imposes a kernel density on the space. The goal is to design fast and memory-efficient data structures that can output approximations to the kernel density at queried points. This thesis presents a data structure based on the classical near neighbor search and locality-sensitive hashing techniques that improves or matches the query time and space complexity for radial kernels considered in the literature. The approach is based on an implementation of (approximate) importance sampling for each distance range and then using near neighbor search algorithms to recover points from these distance ranges. Later, we show how to improve the runtime, using recent advances in the data-dependent near neighbor search data structures, for a class of radial kernels that includes the Gaussian kernel.