Big Data, Cloud & DevOps

DevOps has evolved big time since many of us thought it was just a buzzword. Now we know that is a myth. DevOps has become a main focus and has been shaping the world of software for the last few years. Experts say that DevOps is going to be the mainstream and its popularity is going to reach its peak in 2019. Here is the Google trend shown for the term “DevOps” and a hypothesis of its projected growth in 2019.

Whether they’re ready or not, companies around the world have a new data challenge – one that they must succeed in meeting, if they don’t want to lose huge amounts of money in fines and penalties. Among its many rules, the GDPR, Europe’s new data privacy and security regime, requires that companies delete personal information on European residents within 48 hours of being asked to do so – providing Europeans with the “right to be forgotten,” and failure to do so could cost a company a lot of money.

To drive innovation to a whole new level we’re seeing a rise in the use of Natural Language Processing (NLP) and Artificial Intelligence (AI) to make finding and using trusted, quality data that much easier. In fact, leading industry analyst firms have noted that Machine Learning, AI and NLP are quickly becoming table stakes for analytics. That requires significant heavy lifting at the infrastructure level and it’s not an easy thing to do.

Big data is disrupting every industry it integrates with, altering the corporate landscape by determining market trends, behavioral analysis, and other insights that only improve business decisions. Detecting and understanding large, verified data sets of hidden patterns, as well as unknown correlations, it’s helping startups make better decisions as they seek financial growth and recognition, build effective business strategies, and maintain customer satisfaction. Big data is changing people’s lives for the better, offering a more convenient, yet technical, means to entrepreneurs and investors securing a financial infrastructure within growing companies, while attempting to jump-start brand awareness.

Whether you’re brand-new to the workforce or you’re a veteran, it’s clear modern technology has always played a role in reshaping how we work — and the places where we do that work. With that in mind, let’s talk about the effects modern technologies have had on our workspaces and what they look like, what they’re like to work in and what they might look like someday soon.

In order to start practising data science, it is better if you challenge a real-life problem. This blog post will guide you through the main steps of building a data science project from scratch. It is based on a real-life problem — what are the main drivers of rental prices in Berlin? It will provide an analysis of this situation. It will also highlight the common mistake beginners tend to make when it comes to machine learning.

If you’ve had even a brief encounter with the real estate market, you know how much information is exchanged in the average interaction and how many considerations go into a purchasing decision. If anything, you’ll have to stay sharper than ever to use the new data and analysis tools available to you to end up with the most compelling results. It’s not exactly a surprise, then, that big data is bringing significant change to how the real estate market conducts itself and even how residential and commercial properties are maintained. Here are four of the most interesting.

The data we use is usually split into training data and test data. The training set contains a known output and the model learns on this data in order to be generalized to other data later on. We have the test dataset (or subset) in order to test our model’s prediction on this subset. Let’s see how to do this in Python. Following a short overview on the topic, an example on implementing it in Python is given.

In an era of consumerization of IT, users demand a new way of interaction and organizations may finally have to face the reality that user created profiles are unreliable and they must embrace new methods of authentication that balance security requirements and user experience, opening the path to the use of trusted digital identities. The problem is — how can an organization verify that they are interacting with the proper end user? 

In what will probably feel obvious in hindsight, artificial intelligence and other technologies may be about to compete with or even displace management consultants and some of the services consultancy firms offer. The reality of the situation will take a moment to unpack, but here’s a spoiler — the result should be better insights for the organizations retaining consultants, along with greater capabilities and less busywork for the consultants themselves. Another way to look at this is that AI is adding powerful new tools and vast new opportunities to the modern consultant’s toolkit.

What can our minds trust? What’s real today? The world is filled with technology, media, data and a massive amount of content that provides conflicting viewpoints. Consumers are growing increasingly cautious as marketers leverage big data technology to refine their innovative techniques for separating people from their hard-earned money. The information overload conundrum will grow more relevant as the digital universe expands. One field that’s heavily affected by the proliferation of big data is healthcare. Today’s healthcare leaders must adapt to a new operating environment that’s a moving target with no hope of satisfying the clear majority of consumers – for now.