サクサク読めて、アプリ限定の機能も多数!
トップへ戻る
アメリカ大統領選
atbrox.com
Combining Hadoop/Elastic Mapreduce with AWS Redshift Data Warehouse There are currently interesting developments of scalable (up to Petabytes), low-latency and affordable datawarehouse related solutions, e.g. AWS Redshift (cloud-based) [1] Cloudera’s Impala (open source) [2,3] Apache Thrill (open source) [4] This posting shows how one of them – AWS Redshift – can be combined with Hadoop/Elastic ma
Mapreduce & Hadoop Algorithms in Academic Papers (5th update – Nov 2011) The prior update of this posting was in May, and a lot has happened related to Mapreduce and Hadoop since then, e.g. 1) big software companies have started offering hadoop-based software (Microsoft and Oracle), 2) Hadoop-startups have raised record amounts, and 3) nosql-landscape becoming increasingly datawarehouse’ish and sq
Mapreduce & Hadoop Algorithms in Academic Papers (4th update – May 2011) Follow @atbrox It’s been a year since I updated the mapreduce algorithms posting last time, and it has been truly an excellent year for mapreduce and hadoop – the number of commercial vendors supporting it has multiplied, e.g. with 5 announcements at EMC World only last week (Greenplum, Mellanox, Datastax, NetApp, and Snaplog
Programmatic Deployment to Elastic Mapreduce with Boto and Bootstrap Action A while back I wrote about How to combine Elastic Mapreduce/Hadoop with other Amazon Web Services. This posting is a small update to that, showing how to deploy extra packages with Boto for Python. Note that Boto can deploy mappers and reducers in written any language supported by Elastic Mapreduce. In the example below (i
Atbrox is startup company providing technology and services for Search and Mapreduce/Hadoop. Our background is from Google, IBM and research. GPU – Graphical Processing Unit like the NVIDIA Tesla – is fascinating hardware, in particular regarding extreme parallelism (hundreds of cores) and memory bandwidth (tens of Gigabytes/second). The main programming languages for programming GPUs are C-based
Other recommended writeups : Hadoop World NYC (Hilary Mason) The View from HadoopWorld (Stephen O’Grady) Post Hadoop World Thoughts (Deepak Singh) Hadoop World, NYC 2009 (Dan Milstein) Hadoop World Impressions (Steve Laniel) — Location: Roosevelt Hotel, NYC 1235 Joe Cunningham – Visa – Large scale transaction analysis – responsible for Visa Technology Strategy and Innovation been playing with Hado
Mapreduce & Hadoop Algorithms in Academic Papers (3rd update) Atbrox is startup company providing technology and services for Search and Mapreduce/Hadoop. Our background is from Google, IBM and research. Contact us if you need help with algorithms for mapreduce This posting is the May 2010 update to the similar posting from February 2010, with 30 new papers compared to the prior posting, new ones
The newest and most up-to-date version (May 2010) this blog post is available at http://mapreducebook.org Atbrox is startup company providing technology and services for Search and Mapreduce/Hadoop. Our background is from from Google, IBM and Research. This posting is an update to the similar posting from October 2009, roughly doubling the numbers of papers from the previous posting, the new ones
Parallel Machine Learning for Hadoop/Mapreduce – A Python Example Atbrox is startup providing technology and services for Search and Mapreduce/Hadoop. Our background is from from Google, IBM and Research. Update 2010-June-17 Code for this posting is now on github –http://github.com/atbrox/Snabler This posting gives an example of how to use Mapreduce, Python and Numpy to parallelize a linear machin
The newest and most up-to-date version (May 2010) this blog post is available at http://mapreducebook.org An updated and extended version of this blog post can be found here. Motivation Learn from academic literature about how the mapreduce parallel model and hadoop implementation is used to solve algorithmic problems. Disclaimer: this is work in progress (look for updates) Input Data – Academic P
このページを最初にブックマークしてみませんか?
『atbrox | Accelerates Innovation with Code』の新着エントリーを見る
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く