Due to the rapid decrease in the cost of NGS (Next Generation Sequencing), interest has increased in using data generated from NGS to diagnose genetic diseases. However, the data generated by NGS technology is usually in the order of hundreds of gigabytes per experiment, thus requiring efficient and scalable programs to perform data analysis quickly. This paper presents SparkGA2, a memory efficient, production quality framework for high performance DNA analysis in the cloud, which can scale according to the available computational resources by increasing the number of nodes. Our framework uses Apache Spark's ability to cache data in the memory to speed up processing, while also allowing the user to run the framework on systems with lower amounts of memory at the cost of slightly less performance. To manage the memory footprint, we implement an on-the-fly compression method of intermediate data and reduce memory requirements by up to 3x. Our framework also uses a streaming approach to gradually stream input data as processing is taking place. This makes our framework faster than other state of the art approaches while at the same time allowing users to adapt it to run on clusters with lower memory. As compared to the state of the art, SparkGA2 is up to 22% faster on a large big data cluster of 67 nodes and up to 9% faster on a smaller cluster of 6 nodes. Including the streaming solution, where data pre-processing is considered, SparkGA2 is 51% faster on a 6 node cluster. The source code of SparkGA2 is publicly available at https://github.com/HamidMushtaq/SparkGA2.