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Enterprise businesses across the globe come to realize the necessity of using their vast data stores to gain yet another competitive edge over their less techy competition.

DevOps is like a Holy Grail of the modern software development. Many are actively searching for it, some claim they have found it and a vast majority is still waiting to begin their journey.

While to many businesses these components of Big Data operations seem interchangeable, if not fully the same, Big Data engineering actually differs quite a lot from data warehousing.

The financial industry has always been one of the most powerful influencers and consumers of technological innovation.

Managing Director of Intel Labs, Dr. Michael Mayberry introduced Loihi, the first-of-the-kind neuromorphic central processor chip on Tuesday, September 25th, 2017.

Let this fact carve into your mind: Amazon has acquired Whole Foods and less than in a month they sliced the prices for literally every item in the inventory — and still met the margins threshold!

Artificial Intelligence or AI as we imagine it should be a self-learning sentient machine, which will be able to relieve us from many hard, dangerous or simply monotonous work.

Artificial Intelligence or AI is no longer a term from the sci-fi novels and movies. It is a profitable industry, involving hundreds of high-tech startups and billions of dollars in investments worldwide.

While Big Data is still more of an umbrella buzzword for many productive, easily scalable and cost-efficient tools and solutions, the true meaning depends on who uses the term and for what reason.

When we talk about Big Data analytics, we should first understand why this data is so big and what is the actual need of analyzing it.