In today’s data-driven world, skilled developers are much sought out for their ability to build applications that serve the Big Data needs of organizations. The sheer size, complexity, and diversity of Big Data requires specialized applications and dedicated hardware to process and analyze this information with the aim of uncovering useful business insights that would otherwise be unavailable.
Serverless computing brings a highly efficient way to deploy applications and run software on demand. Testament to that is the fact that serverless application adoption is increasing significantly over the years, with at least 1 in 5 organizations using FaaS (Function as a Service) in major Cloud providers such as Amazon, Azure & GCP.
Software teams have focused on agility since the world embraced Mark Zuckerberg’s motto to “move fast and break things.” But many still lack the confidence or tooling to accelerate their processes. What’s more: in the race to release more, ship faster, and prioritize speed, many have neglected thoughtfulness and security – with Facebook itself becoming the poster child of data misuse.
Kubernetes powers significant automation capabilities for developers in deploying, managing, scaling, and ensuring the availability of containerized apps. Data from 2021 shows that adoption continues to rise with over 5.6 million developers now using the industry’s favored container orchestration engine. However, Kubernetes and containerization introduce new complexities that pose unique security challenges.
Today’s threat actors often carry out cyber attacks with the primary objective of accessing and exfiltrating sensitive information from your IT environment. Efforts to obtain this “crown jewel” information usually involve complex multi-phase cyber attacks. But another way in which sensitive data ends up in the hands of malicious actors with a lot less effort is when an organization exposes sensitive data assets in a data leak incident.
Infrastructure as code (IaC) has become the de-facto method for dealing with infrastructure at scale. This codification of infrastructure configurations lets software development teams create version-controlled, reusable configurations. Moreover, it enables integrating infrastructure management as a part of the delivery pipeline.
DevOps has been the methodology of choice among developers for over a decade. No doubt, it’s proven its efficiency and ability to speed up processes while uniting teams by promoting open communication and shared responsibility. But will GitOps steal the spotlight? We’re exploring the answer in today’s post by looking at these two methodologies’ similarities and differences, advantages, and limitations.
Imagine having to manually provision and configure every device in a large corporation. Then visualize the upgrade process. How about patching? Then, picture ensuring conformity on every device. Next, add some enterprise-wide IT governance changes that must be implemented. The process would be daunting, to say the least, every time.
Rapid and constantly-evolving software development cycles have increased the need for reliable and fast infrastructure changes. Thus manually carrying out infrastructure changes has become an unscalable process – which is what Infrastructure as Code (IaC) tools are here to solve. They enable teams to codify their infrastructure configurations and integrate them directly into their CI/CD pipelines.
Vulnerabilities found in application platforms and third-party libraries have drawn growing attention to application security in the last few years, putting pressure on DevOps teams to detect and resolve vulnerabilities in their Software Development Life Cycle (SDLC). Take the NVD (National Vulnerability Database), which tracks and records all significant vulnerabilities published and disclosed by software vendors.
