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The article covers a guide on setting up TLS/Mutual TLS (mTLS) for securing communication between Kafka clients and servers, specifically in a Kubernetes environment, thus mitigating potential threats such as man-in-the-middle attacks and unauthorized access to data. We’ll be starting off by diving into a bunch of topics that’ll help you understand why we’re doing what we’re doing for our setup. By Aranya Chauhan.

To create a secure line of communication between Kafka Clients and Servers, think of it like building a fortress around your data so that no sneaky middlemen can sneak a peek.

The article then dives into:

• Man-in-the-middle attack
• TLS/mTLS 101
• TLS Certificates
• Setting up TLS Authentication on Kafka Deployed on Kubernetes
• Installing Single Node Kubernetes:
• Installing Helm
• Deploying Strimzi Operator and Kafka Cluster
• Deploying Kafka Cluster
• Configuring Client Side TLS/mTLS Authentication

… and more. When it comes to setting up TLS/mTLS with Kafka, the safety dance doesn’t just stop at configuring all those tricky certificates and keystores. Oh no, it’s also about keeping those secrets, well, secret. Because let’s face it, handling private keys and secrets with the carelessness of leaving your car keys in the ignition isn’t exactly a recipe for security success. Good read!

As part of Zero Trust adoption guidance, this article is part of the Prevent or reduce business damage from a breach business scenario and describes how to protect your organization from cyberattacks. This article focuses on how to deploy additional security measures to prevent a breach and limit its spread and to create and test a business continuity and disaster recovery (BCDR) infrastructure to more quickly recover from a destructive breach. By BrendaCarter, joe-davies-affirm and MicrosoftGuyJFlo.

The article main focus is around guiding principles such as Minimize blast radius and segment access and Verify end-to-end encryption.

The content captures:

• The adoption cycle for implementing security breach prevention and recovery infrastructure
• Define strategy phase
• Motivations for implementing security breach prevention and recovery infrastructure
• Outcomes for implementing security breach prevention and recovery infrastructure
• Plan phase
• Ready phase
• Adopt phase
• Govern and manage phases

… and more. Governance of your organization’s ability to implement breach prevention and recovery is an iterative process. By thoughtfully creating your implementation plan and rolling it out across your digital estate you have created a foundation. Use the following tasks to help you start building your initial governance plan for this foundation. Excellent guide!

This tutorial will take you through the process of building a Django blog back end and a Vue front end, using GraphQL to communicate between them. By Philipp Acsany.

For this project, you’ll create a small blogging application with some rudimentary features: Authors can write many posts, posts can have many tags and can be either published or unpublished.

Further in the article:

• Prerequisites
• Set up the Django Blog
• Build a blog using Django, GraphQL, and Vue
• Set up Graphene-Django
• Set up django-cors-headers
• Set up Vue.js
• Create basic views and components
• Update the Vue components
• Implement Vue Apollo
• Fetch the data

You’ve seen how you can use GraphQL for building typed, flexible views of your data. You can use these same techniques on an existing Django application you’ve built or one you plan to build. Like other APIs, you can use yours in most any client-side framework as well. This is extensive tutorial with all code attached and explained. Nice one!

When you design workload architectures, you should use industry patterns that address common challenges. Patterns can help you make intentional tradeoffs within workloads and optimize for your desired outcome. By ckittel and ShannonLeavitt.

They can also help mitigate risks that originate from specific problems, which can impact security, performance, cost, and operations. If not mitigated, those risks will eventually cause reliability issues.

Some of the patterns covered here:

• Ambassador
• Bulkhead
• Cache-aside
• Circuit breaker
• Claim check
• Compensating transaction
• Event sourcing
• Federated identity
• Pipes and filters
• Throttling
• Geode

… and more. Many design patterns directly support one or more architecture pillars. Design patterns that support the Reliability pillar prioritize workload availability, self-preservation, recovery, data and processing integrity, and containment of malfunctions. Interesting read!

Redux is the most powerful state management tool in the React.Js library, and it’s frequently used by Frontend Engineers to build complex applications. With the help of Redux, we can easily manage the state of our React applications, which helps us to develop and maintain complex applications. By Prasandeep and Dan Ackerson.

This tutorial will provide a clear understanding of Redux’s functionality in React and how it can be used to manage state efficiently, including:

• Prerequisites
• What is Redux?
• Why do we need Redux in React.js?
• Reasons to integrate Redux with your React.js application

There is also code available in GitHub repository. In this tutorial, we covered the complete process of integrating Redux with React-based applications. By following the steps, you can now effortlessly set up a Redux store, define reducers, and connect React components to the Redux store. Good read!

An MIT team precisely controlled an ultrathin magnet at room temperature, which could enable faster, more efficient processors and computer memories. By Adam Zewe.

Experimental computer memories and processors built from magnetic materials use far less energy than traditional silicon-based devices. Two-dimensional magnetic materials, composed of layers that are only a few atoms thick, have incredible properties that could allow magnetic-based devices to achieve unprecedented speed, efficiency, and scalability. This is key, since magnets composed of atomically thin van der Waals materials can typically only be controlled at extremely cold temperatures, making them difficult to deploy outside a laboratory.

The researchers used pulses of electrical current to switch the direction of the device’s magnetization at room temperature. Magnetic switching can be used in computation, the same way a transistor switches between open and closed to represent 0s and 1s in binary code, or in computer memory, where switching enables data storage.

In the future, such a magnet could be used to build faster computers that consume less electricity. It could also enable magnetic computer memories that are nonvolatile, which means they don’t leak information when powered off, or processors that make complex AI algorithms more energy-efficient. Interesting read!

Artificial intelligence (AI) is here, and it is here to stay. “Every industry will become a technology industry,” according to NVIDIA founder and CEO, Jensen Huang. The use cases for AI are virtually limitless, from breakthroughs in medicine to high-accuracy fraud prevention. AI is already transforming our lives just as it is transforming every single industry. It is also beginning to fundamentally transform data center infrastructure. By Anton Chuchkov, Brad Wilson.

AI workloads are driving significant changes in how we power and cool the data processed as part of high-performance computing (HPC). A typical IT rack used to run workloads from 5-10 kilowatts (kW), and racks running loads higher than 20 kW were considered high-density – a rare sight outside of very specific applications with narrow reach. Mark Zuckerberg announced that by the end of 2024, Meta will spend billions to deploy 350,000 H100 GPUs from NVIDIA. Rack densities of 40 kW per rack are now at the lower end of what is required to facilitate AI deployments, with rack densities surpassing 100 kW per rack becoming commonplace and at large scale in the near future.

The transition to accelerated computing will not happen overnight. Data center and server room designers must look for ways to make power and cooling infrastructure future-ready, with considerations for the future growth of their workloads. Getting enough power to each rack requires upgrades from the grid to the rack. In the white space specifically, this likely means high amperage busway and high-density rack PDUs. To reject the massive amount of heat generated by hardware running AI workloads, two liquid cooling technologies are emerging as primary options: Direct-to-chip liquid cooling, Rear-door heat exchangers.

While direct-to-chip liquid cooling offers significantly higher density cooling capacity than air, it is important to note that there is still excess heat that the cold plates cannot capture. This heat will be rejected into the data room unless it is contained and removed through other means such as rear-door heat exchangers or room air cooling. Interesting read!

Nginx Proxy Manager (NPM) is an open-source and free application designed to simplify the management of Nginx’s proxy, SSL, Access Lists, and more. It is built with a user-friendly dashboard that aims to help those users who aren’t exactly Nginx CLI experts. Plus, it also provides free SSL via Let’s Encrypt, Docker integration, and support for multiple users. By Diego Asturias.

In this complete guide, we aim to provide an introduction to Nginx Proxy Manager. We go through the basics of what is NPM, how it works, its features, and more. Author provides information about:

• What is Nginx Proxy Manager?
• NPM versus native NGINX configurations
• How to install Nginx Proxy Manager?
• How to use the Nginx Proxy Manager UI and configure it?
• Vital configurations and initial setup
• Nginx Proxy Manager FAQ

The tool allows users to easily expose web services within their network (or computer). In other words, it provides a secure and efficient gateway for internet traffic. Likewise, its integration with Let’s Encrypt enables users to secure their services with free SSL certificates. Nice one!

The Web Server is a crucial part of web-based applications. Apache Web Server is often placed at the edge of the network; hence it becomes one of the most vulnerable services to attack. A practical guide to secure and harden Apache HTTP Server. By Chandan Kumar.

Having default configuration supply much sensitive information which may help hacker to prepare for an attack of the applications. The majority of web application attacks are through XSS, Info Leakage, Session Management and SQL Injection attacks which are due to weak programming code and failure to sanitize web application infrastructure.

Practical advise in the article contains:

• Remove server version banner
• Disable directory browser listing
• Etag
• Run Apache from a non-privileged account
• Protect binary and configuration directory permission
• System settings protection
• HTTP request methods
• Disable trace HTTP request
• Set cookie with HttpOnly and secure flag
• X-XSS protection
• Mod security

… and more. The article is great helper tool for middleware administrator, application support, system analyst, or anyone working or eager to learn Hardening & Security guidelines. Good read!

Without basic computer architecture best practices, generative AI systems are sluggish. Here are a few tips to optimize complex systems. By David Linthicum.

Performance is often an afterthought with generative AI development and deployment. Most deploying generative AI systems on the cloud, and even not the cloud, have yet to learn what the performance of their generative AI systems should be, take no steps to determine performance, and end up complaining about the performance after deployment. Or, more often, the users complain, and then generative AI designers and developers complain to me.

Author also discusses in this blog:

• Complex deployment landscapes
• AI model tuning
• Vendors could have done a better job establishing best practices
• Security concerns
• Regulatory compliance

Implement automation for scaling and resource optimization, or autoscaling, which cloud providers provide. This includes using machine learning operations (MLOps) techniques and approaches for operating AI models.

At their essence, generative AI systems are complex, distributed data-oriented systems that are challenging to build, deploy, and operate. They are all different, with different moving parts. Most of the parts are distributed everywhere, from the source databases for the training data, to the output data, to the core inference engines that often exist on cloud providers. Nice one!