Hadoop works well when a problem can be broken down into discrete and parallel sub-tasks. Some problems must be applied to an entire dataset. She lists some of these: correlation, covariance, principal component analysis, multivariate statistics, generalized linear models.
I haven’t tried this myself (don’t have a RaspberryPi, but only have an Arduino), and even if it’s possible to get it to install I’m not sure what the runtime could accomplish, but this guy has published a short list of instructions on how to install Hadoop on RaspberryPi.
Western Union has 70 million customers in 200 countries, and processes 29 payment service transactions per second. They are now using Hadoop for real time analytics, which seems surprising as I’d expect a more likely use case to be batch analytics.
Hadoop is generally assumed to run on clusters of generic commodity hardware. Intel has just released a customized/optimized distribution that it claims is up to 30x faster if run on the Xenon E7 v2 family of processors, which is hardly generic or commodity.
Not sure how well this will work, or if the use cases support it. Rather than optimize Hadoop for use cases that it was designed for, Teradata is merging Hadoop into its legacy core data warehouse. Will Hadoop add value or make it overly complex?
Hydra is not built on top of Hadoop, but functions similar to Summingbird, Storm, and Spark.
Data can stream into it, and analytics can be run in real time, rather than only in batch.
AddThis is the company that originally developed Hydra, which is now in open sourced through Apache. AddThis runs six Hydra clusters, one of which is comprised of 156 servers and processes 3.5 billion transactions per day.