Don't forget Hadoop MapReduce. :)
"Uses" for MapReduce (according to Wikipedia):
MapReduce is useful in a wide range of applications including:
distributed grep, distributed sort, web link-graph reversal,
term-vector per host, web access log stats, inverted index
construction, document clustering, machine learning, and statistical
machine translation. Moreover, the MapReduce model has been adapted to
several computing environments like multi-core and many-core systems,
desktop grids, volunteer computing environments, dynamic cloud
environments, and mobile environments.
At Google, MapReduce was used
to completely regenerate Google's index of the World Wide Web.
It replaced the old ad hoc programs that updated the index and ran the
Check out this page for a huge list of organizations using Hadoop, and what they're using it for.
A few of the "B"s, for example:
• BabaCar ◦ 4 nodes cluster (32 cores, 1TB).
◦ We use Hadoop for searching and analysis of millions of rental
• Baidu - the leading Chinese language search engine ◦ Hadoop used to
analyze the log of search and do some mining work on web page database
◦ We handle about 3000TB per week
◦ Our clusters vary from 10 to 500 nodes
◦ Hypertable is also supported by Baidu
• Beebler ◦ 14 node cluster (each node has: 2 dual core CPUs, 2TB
storage, 8GB RAM)
◦ We use hadoop for matching dating profiles
• Benipal Technologies - Outsourcing, Consulting, Innovation ◦ 35
Node Cluster (Core2Quad Q9400 Processor, 4-8 GB RAM, 500 GB HDD)
◦ Largest Data Node with Xeon E5420*2 Processors, 64GB RAM, 3.5 TB HDD
◦ Total Cluster capacity of around 20 TB on a gigabit network with
failover and redundancy
◦ Hadoop is used for internal data crunching, application development,
testing and getting around I/O limitations