We use a Hadoop cluster to rollup registration and view data each night.
Our cluster has 10 1U servers, with 4 cores, 4GB ram and 3 drives
Each night, we run 112 Hadoop jobs
It is roughly 4X faster to export the transaction tables from each of our reporting databases, transfer the data to the cluster, perform the rollups, then import back into the databases than to perform the same rollups in the database.
We use Hadoop in a Data-Intensive Computing capstone course. The course projects cover topics like information retrieval, machine learning, social network analysis, business intelligence, and network security.
The students use on-demand clusters launched using Amazon's EC2 and EMR services, thanks to its AWS in Education program.
We use Hadoop to store copies of internal log and dimension data sources and use it as a source for reporting/analytics and machine learning.
Currently we have 2 major clusters:
A 1100-machine cluster with 8800 cores and about 12 PB raw storage.
A 300-machine cluster with 2400 cores and about 3 PB raw storage.
Each (commodity) node has 8 cores and 12 TB of storage.
We are heavy users of both streaming as well as the Java APIs. We have built a higher level data warehousing framework using these features called Hive. We have also developed a FUSE implementation over HDFS.
We use a customised version of Hadoop and Nutch in a currently experimental 6 node/Dual Core cluster environment.
What we crawl are our clients Websites and from the information we gather. We fingerprint old and non updated software packages in that shared hosting environment. We can then inform our clients that they have old and non updated software running after matching a signature to a Database. With that information we know which sites would require patching as a free and courtesy service to protect the majority of users. Without the technologies of Nutch and Hadoop this would be a far harder to accomplish task.
We are using Hadoop and Nutch to crawl Blog posts and later process them. Hadoop is also beginning to be used in our teaching and general research activities on natural language processing and machine learning.
We use hadoop for Information Retrieval and Extraction research projects. Also working on map-reduce scheduling research for multi-job environments.
Our cluster sizes vary from 10 to 30 nodes, depending on the jobs. Heterogenous nodes with most being Quad 6600s, 4GB RAM and 1TB disk per node. Also some nodes with dual core and single core configurations.
Rather than put ads in or around the images it hosts, Levin is working on harnessing all the data his service generates about content consumption (perhaps to better target advertising on ImageShack or to syndicate that targetting data to ad networks). Like Google and Yahoo, he is deploying the open-source Hadoop software to create a massive distributed supercomputer, but he is using it to analyze all the data he is collecting.
Using Hadoop MapReduce to analyse billions of lines of GPS data to create TrafficSpeeds, our accurate traffic speed forecast product.
Kalooga - Kalooga is a discovery service for image galleries.
Uses Hadoop, Hbase, Chukwa and Pig on a 20-node cluster for crawling, analysis and events processing.
Katta - Katta serves large Lucene indexes in a grid environment.
Uses Hadoop FileSytem, RPC and IO
Koubei.com Large local community and local search at China.
Using Hadoop to process apache log, analyzing user's action and click flow and the links click with any specified page in site and more. Using Hadoop to process whole price data user input with map/reduce.
This is the cancer center at UNC Chapel Hill. We are using Hadoop/HBase for databasing and analyzing Next Generation Sequencing (NGS) data produced for the Cancer Genome Atlas (TCGA) project and other groups. This development is based on the SeqWare open source project which includes SeqWare Query Engine, a database and web service built on top of HBase that stores sequence data types. Our prototype cluster includes:
Multiple alignment of protein sequences helps to determine evolutionary linkages and to predict molecular structures. The dynamic nature of the algorithm coupled with data and compute parallelism of Hadoop data grids improves the accuracy and speed of sequence alignment. Parallelism at the sequence and block level reduces the time complexity of MSA problems. The scalable nature of Hadoop makes it apt to solve large scale alignment problems.
Our cluster size varies from 5 to 10 nodes. Cluster nodes vary from 2950 Quad Core Rack Server, with 2x6MB Cache and 4 x 500 GB SATA Hard Drive to E7200 / E7400 processors with 4 GB RAM and 160 GB HDD.
We are using Hadoop on 17-node and 103-node clusters of dual-core nodes to process and extract statistics from over 1000 U.S. daily newspapers as well as historical archives of the New York Times and other sources.
30 nodes cluster (Xeon Quad Core 2.4GHz, 4GB RAM, 1TB/node storage). We use Hadoop to facilitate information retrieval research & experimentation, particularly for TREC, using the Terrier IR platform. The open source release of Terrier includes large-scale distributed indexing using Hadoop Map Reduce.
We are one of six universities participating in IBM/Google's academic cloud computing initiative. Ongoing research and teaching efforts include projects in machine translation, language modeling, bioinformatics, email analysis, and image processing.
We currently run one medium-sized Hadoop cluster (1.6PB) to store and serve up physics data for the computing portion of the Compact Muon Solenoid (CMS) experiment. This requires a filesystem which can download data at multiple Gbps and process data at an even higher rate locally. Additionally, several of our students are involved in research projects on Hadoop.
We run a 16 node cluster (dual core Xeon E3110 64 bit processors with 6MB cache, 8GB main memory, 1TB disk) as of December 2008. We teach MapReduce and use Hadoop in our computer science master's program, and for information retrieval research.
uses Hadoop as a component in our Scalable Data Pipeline, which ultimately powers VisibleSuite and other products. We use Hadoop to aggregate, store, and analyze data related to in-stream viewing behavior of Internet video audiences. Our current grid contains more than 128 CPU cores and in excess of 100 terabytes of storage, and we plan to grow that substantially during 2008.