Dierk König introduces GPars, Groovy’s library for concurrent programming, explaining a simpler and less error-prone way to use fork/join, map/reduce, actors, and dataflow in Java and Groovy.
In his new article “MapReduce Patterns, Algorithms, and Use Cases”, Ilya Katsov gives a systematic view of the different MapReduce patterns, algorithms and techniques that can be found on the web or in scientific articles along with several practical use case studies.
Hortonworks, a company created in June 2011 by Yahoo! and Benchmark Capital, has announced the Technical Preview Program of Data Platform based on Hadoop. The company employs many of the core Hadoop contributors and intends to provide support and training.
MapR Technologies released a big data toolkit, based on Apache Hadoop with their own distributed storage alternative to HDFS. The software is commercial, with both a free edition, M3, as well as a paid edition, M5. M5 includes snapshots and mirroring for data, Job Tracker recovery, and commercial support. MapR's M5 edition will form the basis of EMC Greenplum's upcoming HD Enterprise Edition.
Yahoo spun-out its core Hadoop team, forming a new company Hortonworks. CEO Eric Baldeschwieler presented their vision of easing adoption of Hadoop and making core engineering improvements for availability, performance, and manageability. Hortonworks will sell support, training, and certification, primarily indirects through partners.
A prevalent trend in IT in the last twenty years was scaling-out, rather than scaling-up. But due to the recent technological advances there is a new option, scaling-out scaled-up servers based on GPUs.
Yahoo recently announced and presented a redesign of the core map-reduce architecture for Hadoop to allow for easier upgrades, larger clusters, fast recovery, and to support programming paradigms in addition to Map-Reduce. The new design is quite similar to the open source Mesos cluster management project - both Yahoo and Mesos commented on the differences and opportunities.
Ricky Ho revisited his three year old post on that question and realized that a lot had changed since then.
Ville Tuulos talks about Disco, the Map/Reduce framework for Python and Erlang, real-world data mining with Python, the advantages of Erlang for distributed and fault tolerant software, and more.
Rob Pike discusses concurrency in programming languages: CSP, channels, the role of coroutines, Plan 9, MapReduce and Sawzall, processes vs threads in Unix, and more programming language history.
Ron Bodkin discusses big data architecture, real-time analytics, batch processing, map-reduce, and data science.
