The vision of the human genome project was born in the early 1980s. One decade later, it was officially started in the U.S. in 1990. Another decade later, a first draft of the human genome was announced in 2000. In the same period costs for computer hardware dropped and capacities of main memory and storage systems underwent an exponential growth. Today, DNA sequencing and genome analysis are turned into reality. For example, malicious tissue from tumor patients is analyzed to derive concrete treatment decisions in course of personalized medicine. Suspects at crime scenes are identified by DNA profiling. Optimized crops are selected based on the results of their genetic analysis to improve harvests in agriculture worldwide. All examples have in common: Genome data is huge and its analysis takes days to weeks. For example, the human genome consists of ~3.2 billion base pairs (= 3.2 GB) distributed across 23 chromosomes, building 20k-30k genes that code 50k-300k proteins. Genome data is a specific subset of scientific data. Data management for scientific data comes with various challenges, such as huge storage requirements, traditional scanning algorithms are based on reading sequences of characters from files, processing of operational data in databases is only rarely considered, parallelization of processing, etc.