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.
The Amazon Web Services (AWS) team announced a set of resources targeting the high performance computing needs of the scientific community. AWS specifically highlights their “spot pricing” market as a way to do cost-effective, massive scale computing in Amazon cloud environment.
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.
Google's Daniel Peng and Frank Dabek published a paper on "Large-scale Incremental Processing Using Distributed Transactions and Notifications” explaining that databases do not meet the storage or throughput requirements for Google's indexing system which stores tens of petabytes of data and processes billions of updates per day on thousands of machines.
Jay Kreps of LinkedIn presented some informative details of how they process data at the recent Hadoop Summit. Kreps described how LinkedIn crunches 120 billion relationships per day and blends large scale data computation with high volume, low latency site serving.
The Hadoop Summit of 2010 started off with a vuvuzela blast from Blake Irving, Chief Product Officer for Yahoo. Yahoo delivered keynote addresses that outlined the scale of their use, technical directions for their contributions, and architectural patterns in how they apply the technology.
Recently Adobe released Puppet recipes that they are using to automate Hadoop/HBase deployments to the community. InfoQ spoke with Luke Kanies, founder of PuppetLabs, to learn more about what this means.
The Apache Mahout project, a set of highly scalable machine-learning libraries, recently announced it's first public release. InfoQ spoke with Grant Ingersoll, co-founder of Mahout and a member of the technical staff at Lucid Imagination, to learn more about this project and machine learning in general.
It has been possible to run Hadoop on EC2 for a while. Today Amazon simplified the process by announcing Amazon Elastic MapReduce which automatically deploys EC2 instances for computational use and includes a API for interacting with them.
Cascading is a new processing API for data processing on Hadoop clusters, and supports building complex processing workflows using an expressive, declarative API.