Hadoop Studio is a map-reduce development environment (IDE) based on Netbeans. It makes it easy to create, understand, and debug map-reduce applications based on Hadoop, without requiring development-time access to a map-reduce cluster. The studio provides a real-time workflow view of a map-reduce job, which displays the individual inputs, outputs, and interactions between the phases of a map-reduce job. The workflow view of a job updates in real time with the developer's code changes. It then generates Java sources and compiles them into a binary jar file, which can be run on a normal Hadoop cluster.
Gfarm is a distributed filesystem, generally used for large scale cluster computing. It's implemented in userland, and can be mounted by FUSE. It utilizes locality of a file to access a data node, and supports Globus GSI for Wide Area Network. Users can explicitly control file replica location on Gfarm. Gfarm can be used as an alternative storage system to HDFS for Hadoop, Samba, MPI-IO, and GridFTP. Monitoring via ZABBIX and Ganglia is also supported.
Syoncloud Logs processes log files from various applications and many servers. It can capture business relevant information from everyday log files generated by Web servers, business applications, and back office applications. It uses Flume sinks that run on the machines that produce log files. This data is filtered and relevant events channeled to HBase. The HBase NoSQL database is used for actual data analysis. The number of HBase nodes depends on the amount of processed log files. Syoncloud Logs has an easy to use installer that includes all necessary components such as Hadoop, Flume, Hbase, and Zookeeper.
MapReduce-BitDew is an implementation of the MapReduce programming model proposed by Google for Internet Desktop Grids. Using MapReduce-BitDew, you can execute MapReduce applications on resources like Desktop PCs distributed on the Internet. MapReduce-BitDew features a firewall-friendly protocol, fault-tolerance, result-certification, 2-level schedulers, and more.
dispy is a Python framework for parallel execution of computations by distributing them across multiple processors in a single machine (SMP), or among many machines in a cluster or grid. The computations can be standalone programs or Python functions. dispy is well suited for the data parallel (SIMD) paradigm where a computation is evaluated with different (large) datasets independently (similar to Hadoop, MapReduce, Parallel Python). dispy features include automatic distribution of dependencies (files, Python functions, classes, modules), client-side and server-side fault recovery, scheduling of computations to specific nodes, encryption for security, sharing of computation resources if desired, and more.