We describe a Big Data-practical, SQL-implementable algorithm for efficiently determining connected components for graph data stored in a Massively Parallel Processing (MPP) relational database. The algorithm described is a linear-space, randomised algorithm, always terminating with the correct answer but subject to a stochastic running time, such that for any $ε>0$ and any input graph $G=\langle V, E \rangle$ the algorithm terminates after $\mathop{\text{O}}(\log |V|)$ SQL queries with probability of at least $1-ε$, which we show empirically to translate to a quasi-linear runtime in practice.