The biggest thing in web apps since “rails can’t scale” is this idea that “your rdbms doesn’t scale.” This has gone so far as to be dubbed the coming of age for “nosql” with lots of blog posts and even a meetup . Indeed, there are many promising key-value stores, distributed key-value stores, document oriented dbs, and column oriented db projects on the radar.
Relational databases, such as MySQL, PostgreSQL and various commercial products, have served us well for many years. Lately, however, there has been a lot of discussion on whether the relational model is reaching the end of its life-span, and what may come after it. Should you care? Which database technology should you be using? Of course the answer is “it depends” , but that’s not very helpful.
Abstract Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers.
Introduction You may have heard about the Stanford University’s machine learning on-line course given by Prof. Andrew Ng. in 2011; it was a great course with lots of real world examples.
Acceleware has recently announced four courses on parallel programming: More information is available on the courses’ webpages. Abstract:
We’re now entering what I call the “Industrial Revolution of Data,” where the majority of data will be stamped out by machines: software logs, cameras, microphones, RFID readers, wireless sensor networks and so on. These machines generate data a lot faster than people can, and their production rates will grow exponentially with Moore’s Law. Storing this data is cheap, and it can be mined for valuable information.