Big Data, Cloud & DevOps

What would happen if the apps that help run our lives and keep our businesses on track suddenly stopped working? You wouldn’t have easy access to things that affect your daily life, like your checking account or health-monitoring systems. From a business perspective, processes would delay, and tasks would take longer to complete. That’s why it is more important now than ever before for developers to assure continuous quality throughout the entire software development life cycle (SDLC). And it all starts with testing.

In the current business paradigm, replicated since by a number of online platforms, individuals willingly provide their personal information in exchange for a service. Personal data is subsequently repackaged and sold to advertisers and marketers. The unavoidable rise of the Internet of Things will only make the issue more complex, as increasingly more intrusive and personal data will start to be collected about each of us.  This poses new challenges around the issue of consent and privacy: 

As a leader, today, both Technical Intelligence and Emotional Intelligence, often referred to as  Emotional Intelligence Quotient or EQ are essential skills for long-term career success.  The importance of EQ for the modern day leader cannot be underestimated. It is one of the fundamental pillars upon which your leadership is built on. If you recognize that a higher EQ would be beneficial for you, your team, and your organization, then today is the day to take those critical first steps in improving it. Enjoy the journey. The destination is far less exciting.

Today’s world revolves around digital technologies. But what if all our apps suddenly stopped working? It is, therefore, imperative that developers deliver continuous quality throughout the entire software development lifecycle. While automation is a key factor in the DevOps lifecycle and makes continuous testing a reality, there are hurdles that deter development teams from embracing an earnest automation initiative. It’s time to make the software development lifecycle continuous. Let’s break down four challenges teams face with AI, open source and continuous testing in the DevOps lifecycle.

In this article you’ll learn how to speed up your Docker build cycles and create lightweight images. One of Docker’s strengths is that it provides caching to help you more quickly iterate your image builds. When building an image, Docker steps through the instructions in your Dockerfile, executing each in order. As each instruction is examined, Docker looks for an existing intermediate image in its cache that it can reuse instead of creating a new (duplicate) intermediate image.

With more businesses adopting devops—and even more experiencing a need for faster development cycles—it’s imperative that executives and devops practitioners work together to ensure success. Despite devops gaining momentum, teams are still struggling to transform their current stack to better accommodate an accelerating pipeline. Upon closer examination, part of the problem is that these two groups are on very different pages when it comes to strategy customer experience and progress. CIOs have an optimistic view of the state of devops. Now’s the time to align CIO goals and devops practitioners’ realities.

Is your organization data-driven? Then you have probably wrestled with the issue of creating a data-driven culture. Having a data-driven culture means that data is the fundamental building block of your team. It means that every team member has a data-driven mindset. It means that every single decision maker uses data as their main evaluation asset. It means that every project uses, generates and pivots on data. It means that your team is constantly leveraging data as a strategic asset. But how do you get there?

It is very difficult for organisations to discern which tools will bring them the most benefit, and which issues they need to plan for.  New technological developments provide the platform for the next generation of innovation, as we’ve seen with the evolution of ‘Big Data’ into advanced analytics, machine learning, and artificial intelligence. How can businesses navigate this increasingly-complex data landscape to make the wisest investments? Here is our guide to the top seven data trends that should be on every organisation’s radar for the year ahead.

Containers are popular among organisations transforming their IT operations from physical, single-tenant computing resources to a more efficient service provider infrastructure model. As application deployment using container technologies grows in production environments, security processes must scale with them. Containerisation provides a number of intrinsic security benefits, such as consistent deployment models, and production container security models should take full advantage of these benefits. To get a full picture of the risks in a container cluster, organisations must automate the process of identifying, mitigating, and alerting on any risks – regardless of source or container origin.

Do you want to become a data scientist? You’re a self-motivated person who is very passionate about data science and bringing values to companies by solving complex problems. Great. But you have ZERO experience in data science and have no clue how to get started in this field.  That’s why this post is dedicated to you — enthusiastic and aspiring data scientists — to answer the most common questions and challenges faced by most people.

We keep hearing about new solutions for test automation and continuous testing. There are plethora of tools evolving these days aiming to solve test authoring, analysis, and maintenance problems. While these are all awesome initiatives that will position testing higher and smarter in the overall DevOps processes, this does not translate into the extinction of the tester. Each of the tools, as well as new tools that will rise are rising to help the existing testers to become more agile, smarter, and efficient.