Genomic data is outpacing traditional Big Data disciplines, producing more information than Astronomy, twitter, and YouTube combined. As such, Genomic research has leapfrogged to the forefront of Big Data and Cloud solutions. This talks outlines how we use Apache Spark to identify genomic association on population-scale whole genome sequencing data, as well as how the accuracy of genome editing approaches can be improved with massively parallel server-less cloud functions. Furthermore, analysing this sheer volume of data has become a complex task, which we solve using artificial intelligence or machine learning. This talk hence also showcases our custom random forest implementations to deal with the 80 million features of genomic data and allow the scoring of genomic target site activity in less than a second.