Yahoo Readies Hadoop for Spin-off, Big Data Gains
Horton hatches the egg, but this is not Dr. Seuss speaking, it’s Yahoo!— opening a doorway to launch Hadoop spin-off anytime this week. With tougher challengers in the ring of open cloud including LexisNexis, Yahoo prepares Hadoop for some exciting updates to beef up for the battle for Big Data supremacy.
Reports have mentioned that for months now that Yahoo! has been trying to incorporate next generation features and capabilities to commercialize Hadoop and give birth to what’s been dubbed Horton Works.
Derrick Harris in a Gigaom article discussed the outcome and targets behind this new Hadoop derivative: “ Its products, which likely will include higher-level management tools on top of the core MapReduce and file system layers, will be open source and HortonWorks will try to maintain a close working relationship with Apache.
“The goal is to make HortonWorks the go-to vendor for a production-ready Hadoop distribution and support, but also to advance Yahoo’s repeated mission of making the official Apache Hadoop distribution the place to go for core software.”
The industry heavyweights are shifting investments towards big data initiatives as a result of these trends. The recent show of support by DreamHost to OpenStack is a testament of how open cloud and big data are harnessing traction hand-in-hand. In a parallel situation are Cloudera with RainStor as their provider, EMC’s Project Lightning and VMware’s Cloud Foundry. Just last May, Hadoop-Datameer has raised additional $9 million funds for their business expansion. Today, Platform Computing announced commercial support for Apache Hadoop distributed file system or HDFS.
Yahoo!’s now gearing up towards launching its very own open source cloud initiative. This project will be a big step for them going into the Big Data arena and for the industry. Hadoop has been a very important development for the cloud and big data capabilities, spawning startups like GoldenOrb to push their own open source agenda. This development only shows that there is already a huge market demand and potential need in the future for data-intensive distributed platforms such as Hadoop.
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