Back in the early 1990s, you would sometimes hear this gag: “Two major products that came out of Berkeley: LSD and UNIX. We don’t believe this to be a coincidence.” Although wildly inaccurate, this joke does reflect Berkeley’s reputation for innovation and revolution. Now from U.C. Berkeley comes what might be the most significant new big data technology since Hadoop: The Berkeley Data Analytics Stack (BDAS).
Hadoop Career Scope in India
Hadoop became the de facto foundation of today’s big data stack by providing a flexible, scalable, and economical framework for processing massive amounts of structured, unstructured and semistructured data. The Hadoop 1.0 MapReduce algorithm is a relatively straightforward, but powerful, approach to parallel processing. MapReduce is not the most elegant or sophisticated approach for all workloads, but it can be adapted to almost any problem, and can usually scale through the brute force application of many servers.
However, it’s long been realized that MapReduce is not a sufficient solution for emerging big data analytic challenges. MapReduce excels at batch processing but falls short in real time and near-real-time scenarios. Even the simplest MapReduce task takes significant ramp-up time, and, for some machine learning algorithms, execution time is simply inadequate.
