Sign up for our newsletter and get the latest big data news and analysis.

Case Study: More Efficient Numerical Simulation in Astrophysics

Novosibirsk State University is one of the major research and educational centers in Russia and one of the largest universities in Siberia. When researchers at the University were looking to develop and optimize a software tool for numerical simulation of magnetohydrodynamics (MHD) problems with hydrogen ionization —part of an astrophysical objects simulation (AstroPhi) project—they needed to optimize the tool’s performance on Intel® Xeon Phi™ processor-based hardware.

Big Data Project Failure Pain Points and their Solution

Big data projects don’t typically fail for a single reason, and certainly not for technology alone. A combination of factors serve to derail big data deployments. Problems and failures occur due to factors including business strategy, people, culture, inattention to analytics details or the nuances of implemented tools, all intensified by the rapid advancement of digital transformation.

Moving Big Data into the Cloud

While there are multiple architectures targeting big data analytics, we have traditionally focused on maximizing dedicated hardware and large processing power. In the past few years, however, there is a fast growing convergence of big data and cloud, particularly where the data sets are unstructured with simple data models — an area of specific focus for the Apache Hadoop technology.

7 Steps From Raw Data to Insight

Data scientists generally ascribe to the “machine learning process” which is seen as a roadmap to follow when working on a data science project. The infographic at the end of this article provides a detailed work flow that it is general enough to encompass pretty much any data science project.

Data Integration to Improve Customer Experience

In the world of big data, a major goal for most enterprises is to maximize the value of its customer data in order to gain a 360 degree view of the customer. Most customer data, however, are housed in separate data silos. While each data silo contains important pieces of information about your customers, if you don’t connect the data into a single view, you’re only seeing portions of the customer equation.

Bringing Artificial Intelligence to Life

Artificial Intelligence (AI) may seem like a vision for a distant future, but in truth, AI is all around us as machines are increasingly learning to sense, learn, reason, act and adapt in the real world. This is transforming industries and changing our lives in amazing new ways, by amplifying human capabilities, automating tedious or dangerous tasks, and solving some of our most challenging societal problems. In this article, we’ll discuss the path to AI with Intel technologies.

Intel Case Study: Speeding Up a Big Data Platform

In this technology cast study, we’ll examine MeritData, Inc., a leading big data analysis technology and service provider in China. The company’s product is called Tempo, a big data platform that has been widely used by well-known power, manufacturing, financial, and global enterprises and by cloud service providers. MeritData helps its customers explore and exploit data value―and ultimately creates value through data processing, data mining, and data visualization solutions. This is achieved through the fusion of high-performance computing (HPC) technology, leading data analysis algorithms, high-dimensional visualization, and creative data visualization language.

How Data Science Can Save the Traditional Banking Industry

In today’s technologically advancing world, traditional banking groups are being seriously challenged. As Google, Amazon, Facebook, Apple offer more and more banking services and financial technology startups gain traction, the banking industry must take a look at how it can stay competitive. To do this, banking needs to rely on data science.

Scale-out Data Lake Solution Built on EMC Isilon

In this special technology white paper, “Scale-out Data Lake Solution Built on EMC Isilon,” you’ll learn all the essentials a data lake based on EMC Isilon provides- CAPEX savings, OPEX savings, elimination of storage silos, regulatory compliance, and multi-protocol access.

Unified security and Management Solution for BI that is Native on Hadoop

Arcadia Data continues to deliver on its vision of on-cluster analytics by eliminating the complexities of redundant, overlapping security management inherent in BI stacks which rely on moving data off-cluster.