In Lambda Architecture, there are two main layers – Batch and Speed. The first one transforms data in scheduled batches whereas the second is responsible for near real-time data processing. The batch layer is typically used when the source system sends the data in batches, access to the entire dataset is needed for required data processing, or the dataset is too large to be handled as a stream. On the contrary, stream processing is needed for small packets of high-velocity data, where the packets are either mutually independent or packets in close vicinity form a context. Naturally, both types of data processing are computation-intensive, though the memory requirement for batch is higher than the stream layer. Architects look for solution patterns that are elastic, fault-tolerant, performing, cost-effective, flexible, and, last but not least – distributed. ... Lambda architecture is complex because it has two separate components for handling batch and stream processing of data. The complexity can be reduced if one single technology component can serve both purposes.

Tokenized identification puts the power in the user’s hands. This is crucial not just for workplace access and identity, but for a host of other, even more important reasons. Tokenized Digital Ids are encrypted and can only be used once, making it nearly impossible for anyone to view the data included in the digital ID should the system be breached. It’s like Signal, but for your digital IDs. As even more sophisticated technologies roll out, more personal data will be produced (and that means more data is vulnerable). It’s not just our driver’s licenses, credit cards or Social Security numbers we must worry about. Our biometrics and personal health-related data, like our medical records, are increasingly online and accessed for verification purposes. Encrypted digital IDs are incredibly important because of the prevalence of hacking and identity theft. Without tokenized digital IDs, we are all vulnerable. We saw what happened with the Colonial Pipeline ransomware attack recently. It crippled a large portion of the U.S. pipeline system for weeks, showing that critical parts of our infrastructure are extremely vulnerable to breaches.

In some ways, Agile was a grassroots labor movement. It certainly started with the practitioners on the ground and got pushed upwards into management. How did this ever succeed? It’s partially due to developers growing in number and value to their businesses, gaining clout. But the biggest factor, in my view, is that the traditional waterfall approach simply wasn’t working. As software got more complicated and the pace of business accelerated and the sophistication of users rose, trying to plan everything up front became impossible. Embracing iterative development was logical, if a bit scary for managers used to planning everything. I remember meetings in the mid-2000s where you could tell management wasn’t really buying it, but they were out of ideas. What the hell, let’s try this crazy idea the engineers keep talking about. We’re not hitting deadlines now. How much worse can it get? Then to their surprise, it started working, kind of, in fits and starts. Teams would thrash for a while and then slowly gain their legs, discovering what patterns worked for that individual team, picking up momentum.

We're not yet able to measure the behavior of Quantum fractals in the brain – if they exist at all. But advanced technology means we can now measure quantum fractals in the lab. In recent research involving a scanning tunneling microscope (STM), my colleagues at Utrecht and I carefully arranged electrons in a fractal pattern, creating a quantum fractal. When we then measured the wave function of the electrons, which describes their quantum state, we found that they too lived at the fractal dimension dictated by the physical pattern we'd made. In this case, the pattern we used on the quantum scale was the Sierpiński triangle, which is a shape that's somewhere between one-dimensional and two-dimensional. This was an exciting finding, but STM techniques cannot probe how quantum particles move – which would tell us more about how quantum processes might occur in the brain. So in our latest research, my colleagues at Shanghai Jiaotong University and I went one step further. Using state-of-the-art photonics experiments, we were able to reveal the quantum motion that takes place within fractals in unprecedented detail.

Cybercriminals are always quick to leap onto any bandwagon that they can use to improve or modernize their attacks. Audio fakes are becoming so good that it requires a spectrum analyzer to definitively identify fakes, and AI systems have been developed to identify deepfake videos. If manipulating images lets you weaponize them, imagine what you can do with sound and video fakes that are good enough to fool most people. Crimes involving faked images and audio have already happened. Experts predict that the next wave of deepfake cybercrime will involve video. The working-from-home, video-call-laden “new normal” might well have ushered in the new era of deepfake cybercrime. An old phishing email attack involves sending an email to the victim, claiming you have a video of them in a compromising or embarrassing position. Unless payment is received in Bitcoin the footage will be sent to their friends and colleagues. Scared there might be such a video, some people pay the ransom.

Enterprises need to have a robust endpoint data protection and system security. This includes antivirus software and even whitelisting software where only approved applications can be accessed. Enterprises need both an active element of protection, and a reactive element of recovery. Companies hit with a ransomware attack can spend five days or longer recovering from an attack, so it’s imperative that companies are actively implementing the right backup and recovery strategies before a ransomware attack. Black hats who are developing ransomware are trying to prevent any means of egress from an enterprise having to pay the ransom. ... We urge organizations to implement a more comprehensive backup and recovery approach based on the National Institute of Standards and Technology (NIST) Cybersecurity Framework. It includes a set of best practices: Using immutable storage, which prevents ransomware from encrypting or deleting backups; implementing in-transit and at-rest encryption to prevent bad actors from compromising the network or stealing your data; and hardening the environment by enabling firewalls that restrict ports and processes.

“To move to Kubernetes, an organization needs a full engineering team just to keep the Kubernetes clusters running, and that’s assuming a managed Kubernetes service and that they can rely on additional infrastructure engineers to maintain other supporting services on top of, well, the organization’s actual product or service,” they write. While this is part of StackOverflow’s reasoning — “The effort to set up Kubernetes is less than you think. Certainly, it’s less than the effort it would take to refactor your app later on to support containerization.” — Ably argues that “it seems that introducing such an enormously expensive component would merely move some of our problems around instead of actually solving them.” Meanwhile, another blog post this week argues that Kubernetes is our generation’s Multics, again centering on this idea of complexity. Essentially, the argument here is that Kubernetes is “a serious, respectable, but overly complex system that will eventually be replaced by something simpler: the Unix of distributed operating systems.” Well then, back to Unix it is!