Speaker
Mr
Romeo Kienzler
(IBM)
Description
Apache Hadoop seems to evolve to the de-facto standard for large scale OLAP processing. Ignoring the overhead of three fold data block redundancy and inter-node communication between the Shuffle
and Reduce phase Hadoop introduces significant drawbacks when it comes to random data access patterns and concurrency. Although the Brewer's theorem implies that Consistency, Availability and
Partition Tolerance on a Distributed Information System is never achievable at the same time this talk shows how to build an Information System leveraging Linear Scale-Out and Fault Tolerance
on the one side and Concurrency and Consistency on the other side. After basic information management concepts like data locality, concurrency and recovery are introduced in the context of shared disks clusters this talk focuses on applying these concepts on real world scenarios based on IBM's GPFS based pureScale technology.
Author
Mr
Romeo Kienzler
(IBM)