  The ever-increasing volumes of scientific data present new challenges for
distributed computing and Grid technologies. The emerging Big Data revolution
drives exploration in scientific fields including nanotechnology, astrophysics,
high-energy physics, biology and medicine. New initiatives are transforming
data-driven scientific fields enabling massive data analysis in new ways. In
petascale data processing scientists deal with datasets, not individual files.
As a result, a task (comprised of many jobs) became a unit of petascale data
processing on the Grid. Splitting of a large data processing task into jobs
enabled fine-granularity checkpointing analogous to the splitting of a large
file into smaller TCP/IP packets during data transfers. Transferring large data
in small packets achieves reliability through automatic re-sending of the
dropped TCP/IP packets. Similarly, transient job failures on the Grid can be
recovered by automatic re-tries to achieve reliable six sigma production
quality in petascale data processing on the Grid. The computing experience of
the ATLAS and CMS experiments provides foundation for reliability engineering
scaling up Grid technologies for data processing beyond the petascale.
