Welcome back to the “Ask a Data Scientist” article series. This week’s question is from a reader who asks for an overview of unsupervised machine learning.
In this special guest feature, Joe DeCosmo of Enova International reflects on how the business world relies heavily on technology to determine customer preferences as a means to deliver personalized service. Joe DeCosmo is chief analytics officer at Enova International, a global financial resource service for under-served consumers.
“The Hadoop MapReduce framework grew out of an effort to make it easy to express and parallelize simple computations that were routinely performed at Google. It wasn’t long before libraries, like Apache Mahout, were developed to enable matrix factorization, clustering, regression, and other more complex analyses on Hadoop. Now, many of these libraries and their workloads are migrating to Apache Spark because it supports a wider class of applications than MapReduce and is more appropriate for iterative algorithms, interactive processing, and streaming applications.”
In this special guest feature, Al Nugent, co-author of the guide “Big Data for Dummies,” looks back at 2014 and how big data has progressed and also offers some predictions for how the technology might evolve.
“When organizations operate both Lustre and Apache Hadoop within a shared HPC infrastructure, there is a compelling use case for using Lustre as the file system for Hadoop analytics, as well as HPC storage. Intel Enterprise Edition for Lustre includes an Intel-developed adapter which allows users to run MapReduce applications directly on Lustre. This optimizes the performance of MapReduce operations while delivering faster, more scalable, and easier to manage storage.”
“The vision for the Internet of Things is very powerful – a world in which assets, devices, machines, and cloud-based applications seamlessly interoperate, enabling new business models and services; with big data analytics as a foundation to support intelligent decision making in this connected world. As with every vision, the question is how to make it happen. This presentation provides key success factors for IoT, as well as a detailed overview of concrete IoT uses cases in the areas of automotive and transport, manufacturing and supply chain, as well as energy. Finally, a framework for IoT implementation is presented, which helps making your IoT projects a success.”