Big Data, Cloud & DevOps

As Artificial Intelligence continues to automate aspects and functions of various jobs, education is changing faster than ever, with new ideas, technologies, and demands constantly emerging.  At its heart, education is about opportunity, and online learning can potentially make crucial opportunities available to those who would not normally have access to them. This is why it’s crucial that educators see technology not as a threat, but as a tool for enhancing their own pedagogical capacity.

The open science revolution can be said to have begun with open access—the idea that academic papers should be freely available as digital documents. It takes the original idea to the next level, by making that information freely accessible to all. The internet can potentially give everyone with an online connection cost-free access to every article posted online. The same can be said of another important aspect of open science: open data. Before the internet, handling data was a tedious and time-consuming process. But once digitized, even the most capacious databases can be transmitted, combined, compared and analyzed very rapidly. 

Kubernetes is the premier technology for deploying and managing large apps. In this article, we’ll get up and running with K8s on your local machine.  You will know how to set up K8s and run your first K8s app. Also, you will know how to inspect, create, and delete your K8 resources with common commands. Then you’ll deploy your first app. Finally, you’ll see the top K8s commands to know.

With the scale of data growing at a rapid and ominous pace, we needed a way to process potential petabytes of data quickly, and we simply couldn’t make a single computer process that amount of data at a reasonable pace. This problem is solved by creating a cluster of machines to perform the work for you, but how do those machines work together to solve the common problem? Spark is the cluster computing framework for large-scale data processing.

We have a lot of data, and we aren’t getting rid of any of it. We need a way to store increasing amounts of data at scale, with protections against data-loss stemming from hardware failure. Finally, we need a way to digest all this data with a quick feedback loop. Thank the cosmos we have Hadoop and Spark. Apache Spark is a wonderfully powerful tool for data analysis and transformation.

The future is in supercomputers, but until recently, only a handful of agencies have been able to tap into that kind of power. Traditionally, high performance computing (HPC, or supercomputing) has required significant capital investment for large-scale supercomputing infrastructures and operating expenses, and scientists and engineers skilled in HPC application development. Previously, few agencies had these resources and technical expertise. But times have changed, and now the software can be ported out and mainstreamed, and it’s a lot easier to make use of supercomputing in other places.

Leaders who figure out how to leverage increasing data trove to improve their decisions and outcomes will produce superior returns, just like the best investors do that have long relied on machines and “quants.” Failing to make use of the growing surge of data will mean a significant handicap for any leader and their team just like it does in the financial markets. The answer is for corporate leaders to use artificial intelligence to facilitate and speed up the steps above and in the process, make faster, better decisions.

The proper use of data can make you and your organization very successful. Being aware of the areas that need to be improved and the areas that your customers love is a good thing. If you ignore the signal in your data, you risk seeing your operations and your products wither away before your eyes. Data can be your ally and it is now widely recognized as the most important asset that any organization, public or private, possesses. However, we need more leaders with the ability to shepherd the good and virtuous process of executing on a data mission.

Tax fraud is already prevalent, and fraudsters are more sophisticated and automated than ever. To get ahead of the game in detecting fraud and protecting revenue, tax agencies need to leverage more advanced and predictive analytics. Legacy processes, systems, and attitudes need not stand in the way. What’s new in fraud prevention and what does a complete capability look like? What can Tax agencies do differently and better today than they could a few years ago? This blog explores the challenges, opportunities, and value of tax fraud analytics

When talking with CIOs and other senior executives, cloud computing is often cited as the foundation of a given digitization strategy.  Cloud computing and the costs don’t have to remain a love-hate relationship. When planned well beforehand and deployed in a smart fashion, the cloud will make perfect economic sense. This doesn’t just apply for possible cost savings but also — and perhaps even more importantly — for enabling top-line growth. Thinking along the categories people, processes, and tools, will enable companies to come up with a comprehensive game plan that helps overcome the challenges. 

Cloud supports rapid experimentation and innovation by allowing companies to quickly try and even adopt new solutions without significant up-front costs. The #Cloud can be a highly agile wrapper around different systems, different behavior and bringing it all together in an engagement cycle. By changing the way people interact with technology, cloud enables new forms of consumer engagement, expand collaboration across the value chain and bring innovation to companies’ core business models. However, there are myths surrounding cloud computing and clouding the reality of the cloud.