  Motivation: Storage of genomic data is a major cost for the Life Sciences,
effectively addressed mostly via specialized data compression methods. For the
same reasons of abundance in data production, the use of Big Data technologies
is seen as the future for genomic data storage and processing, with
MapReduce-Hadoop as leaders. Somewhat surprisingly, none of the specialized
FASTA/Q compressors is available within Hadoop. Indeed, their deployment there
is not exactly immediate. Such a State of the Art is problematic. Results: We
provide major advances in two different directions. Methodologically, we
propose two general methods, with the corresponding software, that make very
easy to deploy a specialized FASTA/Q compressor within MapReduce-Hadoop for
processing files stored on the distributed Hadoop File System, with very little
knowledge of Hadoop. Practically, we provide evidence that the deployment of
those specialized compressors within Hadoop, not available so far, results in
major cost savings, i.e., on large plant genomes, 30% less HDFS data blocks
(one block=128MB), speed-up of at least x1.5 in I/O time and comparable or
reduced network communication time with respect to the use of generic
compressors available in Hadoop. Finally, we observe that these results hold
also for the Apache Spark framework, when used to process FASTA/Q files stored
on the Hadoop File System.
