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Is 2017 the Year of GPU Databases?

In this special guest feature, Ami Gal, CEO of SQream Technologies, discusses how GPUs and databases are a match made in IT heaven and how GPU databases are poised to take over high performance compute. The reasoning is simple: GPUs can read and process data at speeds far greater than CPUs and are increasing in performance at a rate of roughly 40% per year (equal to the growth rate of data).

Big Data or Small Data? The Correct Answer is Both

In this special guest feature, Dr. Ricardo Baeza-Yates, CTO at NTENT, discusses how it’s not enough to weigh data decisions on the descriptor of big versus small alone – a number of other things must be considered.

The Difference Between Data Science and Data Analytics

In this contributed article, tech writer Rick Delgado, examines the differences between the terms: data science and data analytics, where people working in the tech field or other related industries probably hear these terms all the time, often interchangeably. Although they may sound similar, the terms are often quite different and have differing implications for business.

Should You Use Python or R for Your Programming Language?

In this contributed article, technology writer and blogger Kayla Matthews discusses the age-old “R vs. Python” debate that has circulated around in the data science community for the past few years. “When it comes to choosing a programming language, there really are only two choices if you’re working with data. For data science, machine learning, statistics, IoT technology and even automation, the two best languages to use are Python and R.”

The Exponential Growth of Data

This is the first entry in an insideBIGDATA series that explores the intelligent use of big data on an industrial scale. This series, compiled in a complete Guide, also covers the changing data landscape and realizing a scalable data lake, as well as offerings from HPE for big data analytics. The first entry is focused on the recent exponential growth of data.

Learn Data Science: Eight (Easy) Steps

Our friends over at DataCamp have produced the “Become a Data Scientist in 8 Steps” infographic providing a view of the eight steps that you need to to through to learn data science. Some of these eight steps will be easier for some than for others, depending on background and personal experience, among other factors.

How Predictive Analytics is Changing the Retail Industry

In this special guest feature, Dean Abbott of SmarterHQ discusses how data science and predictive modeling have become the holy grail for the retail industry.