Big Data, Cloud & DevOps

Changes in technology have affected nearly every aspect of our personal and professional life; however, the most prevalent changes are likely in communication. The way that we communicate is constantly evolving, and it is presently being pushed forward by technological advances. When researching the changes that technology has delivered to communication, the focus is often on personal experiences. This is certainly not the full picture. Technology has made significant, permanent changes to how businesses communicate, both internally and externally. The following are just a few of the ways that business communication has changed recently due to technology.

If you’re doing all of the standard “I want to become a data scientist” things, then this means you shouldn’t expect to land your dream job. The market is currently full of junior talent, and as a result, the median aspiring data scientist is unlikely to get much traction. So if you want to avoid the median outcome, why do median things? The problem is, most people don’t think this way when they embark on their data science journeys.

Almost a year after the ominous GDPR regulation was passed in the EU, it’s time to look back and measure its true impact. The ambitious and incredibly strict regulation was meant to bolster privacy and security for European citizens. It also promised to deliver more control over personal data, allowing citizens to outright delete their information if desired. Did it do that? What has changed between its rollout in May 2018 and now?

DevOps and modern application development processes have made storage and data important to both developers and operations teams. Much of today’s application development happens within Linux containers. Those applications require persistent and native container storage to continue to function unabated after the container spins down.  Simultaneously, developers’ storage needs require operations teams to act more quickly than before while still maintaining control over storage resources. At the end of the day, storage may not solve every single DevOps challenge. But it’s certainly a good place to start.

Emerging and innovative technologies are constantly changing the world of business, and more often than not it’s for the better. While we regularly hear about how things like IoT and smart devices change industries such as manufacturing, logistics and even healthcare, there’s one area we don’t hear a lot about: non-profit. Believe it or not, the very same technologies are having an impact on the non-profit sector too. Platforms such as big data, the blockchain, artificial intelligence, and yes, even the Internet of Things all have a role to play in the future of the industry.

2018 is likely to be remembered as the year that artificial intelligence and machine learning finally took root in the DevOps consciousness. Organizations are still struggling to increase the degree of their test automation for desktop web apps, whether responsive or progressive, as well as native mobile apps. Mastering agile and DevOps processes and implementing stable continuous testing strategies are amongst the top challenges teams are facing. For 2019, we predict that there will be few significant advancements in the software industry that will enhance overall maturity in DevOps and Continuous Testing.

There are huge social and financial benefits that businesses and economies can realize if they can successfully leverage Open Data. Despite this, there are still some hurdles for data professionals to leap. A great way to start is to consider whether your data meets the criteria for what’s known as the FAIR principles.  While the FAIR principles are not equal to the idea of Open Data, they are a great start for ensuring that data sets are available en-masse to the data professionals who need them.

Big data Is just one tool for managing risk. As we mentioned above, there are three pillars to effective risk management in modern times: oversight for regulatory compliance, a strong company culture focused on making the right hires and training on the right principles, and the wise application of useful technologies. One of the most important advantages of bringing data analytics into the mix versus relying on the other two pillars alone is that the company’s analytics platform gets smarter with each new data point it receives.

A Dockerfile instruction is a capitalized word at the start of a line followed by its arguments. Each line in a Dockerfile can contain an instruction. Instructions are processed from top to bottom when an image is built. In this article, I’m assuming you are using a Unix-based Docker image. You can also use Windows-based images, but that’s a slower, less-pleasant, less-common process. So use Unix if you can. Let’s do a quick once-over of the dozen Dockerfile instructions we’ll explore.

There is no single technology that will redefine IT operations or business strategies. Instead, technologies must be stacked together to produce new and innovative solutions. Emerging technologies hold a great deal of promise, but only if the right skills are in place to leverage them. Building skills from the ground up will help IT pros understand new trends and incorporate those trends into existing architecture so that the business can grow. Three different trends show how emerging technologies will show up in technology solutions.

R and Python have become the two most popular and favourite languages for the data scientists and data analysts. Both of these are similar, yet, different in their ways which makes it difficult for the developers to pick one out of the two. R is considered to be the best programming language for any statistician. On the other hand, Python does pretty much the same work as R, but data scientists or data analysts prefer it because of its simplicity and high performance.