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“Hide the Tech” to Take Big Data Mainstream

In this contributed article, Ravi Dharnikota, Chief Enterprise Architect at SnapLogic, examines a number of important trends that are simplifying the adoption of big data such as the cloud, serverless computing, self-service integration, and machine learning/AI. The effect is that these trends tend to actually “hide” the tech to put more focus on the data and the business.

Using Python to Drive New Insights and Innovation from Big Data

In a recent white paper “Management’s Guide – Unlocking the Power of Data Science & Machine Learning with Python,” ActiveState – the Open Source Language Company – provides a summary of Python’s attributes in a number of important areas, as well as considerations for implementing Python to drive new insights and innovation from big data.

Four Ways Big Data Is Still Changing the Way We Do Business

In this contributed, Anthony Coggine, HR professional turned business analyst, points out that from the creation of more personalized marketing campaigns, to harnessing the power of social media, here are four of the major reasons that big data is changing the way we do business as we know it:

CASK Data Application Platform Moves Forward

In this video from the DataWorks Summit 2017 conference in San Jose (June 13-15, 2017), insideBIGDATA’s Managing Editor and resident data scientist Daniel D. Gutierrez chats with Nitin Motgi, Founder/CTO at CASK Data.

Book Review: Statistical Learning with Sparsity – The Lasso and Generalizations

As a data scientist, I have a handful of books that serve as important resources for my work in the field – “Statistical Learning with Sparsity – The Lasso and Generalizations” by Trevor Hastie, Robert Tibshirani, and Martin Wainwright is one of them. This book earned a prominent position on my desk for a number of reasons.

When Big Data Doesn’t Work

The use of big data is no longer a strategy for advanced and technologically innovative companies to gain a competitive edge. It is a necessity for all businesses across all industries. Collecting data is not enough. Businesses need to understand how to translate their data into competitive strategies and advantages. In the infographic below, courtesy of our friends at Villanova University, you’ll review the challenges of big data.

WekaIO Eliminates the Need for Legacy External Storage with Cloud-Scale Storage Software

WekaIO, a venture backed high-performance cloud storage software company, emerged from stealth to introduce the industry’s first cloud-native scalable file system that delivers unprecedented performance to applications, scaling to Exabytes of data in a single namespace.

Using Machine Learning with Health Data: The Challenges and Pitfalls

In this contributed article, Elad Ferber, CTO and Co-founder of Spry Health, points out that when considering health data, the level of required customization for machine learning algorithms is very high for 3 reasons: the inherent complexity of the human body, the accessibility and relevance of data sources, and integration into the existing healthcare system.

Interview: David Blei, Professor, Columbia University

The following is a discussion with David Blei, Professor, Columbia University; ACM-Infosys 2013 Foundation Award, ACM Fellow (2015). The Association of Computing Machinery (ACM) just concluded a celebration of 50 years of the ACM A.M. Turing Award (commonly known as the “Nobel Prize of computing”) with a two-day conference in San Francisco. The conference brought together some of the brightest minds in computing to explore how computing has evolved and where the field is headed.

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.