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Fargate vs Lambda : Qui sera le vainqueur ?

Fargate vs Lambda : Qui sera le vainqueur ?

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Fargate et Lambda sont deux technologies très populaires parmi les développeurs cloud. Quel est le meilleur pour votre projet ? Découvrons qui sera le vainqueur !

## Comparaison Fargate vs Lambda dans l’espace sans serveur

Quelles sont les différences entre Fargate et Lambda ?

Fargate et Lambda sont deux options de calcul sans serveur populaires disponibles dans l’écosystème AWS. Bien que les deux outils offrent un calcul sans serveur, ils diffèrent en ce qui concerne les cas d’utilisation, les limites opérationnelles, les allocations de ressources d’exécution, le prix et les performances. Fargate est une moteur de calcul sans serveur proposé par Amazon qui vous permet de gérer efficacement les conteneurs sans les tracas de la mise en provision des serveurs et de l’infrastructure sous-jacente. Lambda, quant à lui, est une plateforme de calcul sans serveur qui vous permet d’exécuter du code sans avoir à gérer des serveurs. Lambda est conçu pour prendre en charge des charges de travail à courtes durées et à faible consommation de ressources.

Quelle est la meilleure option pour l’architecture ?

Lorsqu’il s’agit de choisir entre Fargate et Lambda, il est important de comprendre leurs différences et leurs avantages. Pour les applications à longue durée et à haute consommation de ressources, Fargate est la meilleure option car il offre une gestion des conteneurs plus efficace et une meilleure performance. Cependant, pour les applications à courtes durées et à faible consommation de ressources, Lambda est la meilleure option car il offre une exécution plus rapide et une meilleure utilisation des ressources. En fin de compte, le choix entre Fargate et Lambda dépend des exigences spécifiques de votre application et de votre architecture. Il est important de prendre en compte le coût, la performance et les fonctionnalités avant de prendre une décision finale.

Quelle que soit l’application ou l’architecture que vous souhaitez mettre en place, Fargate et Lambda sont tous deux des outils puissants qui peuvent vous aider à atteindre vos objectifs. En tant qu’informaticien enthousiaste, je trouve que ces outils sont très utiles pour créer des applications modernes et évolutives. Fargate et Lambda offrent tous les deux des fonctionnalités avancées qui peuvent être utilisées pour créer des architectures robustes et flexibles. Les deux outils sont faciles à utiliser et peuvent être intégrés à d’autres services AWS pour offrir une expérience utilisateur optimale. En fin de compte, le choix entre Fargate et Lambda dépendra des exigences spécifiques de votre application et de votre architecture.

Source de l’article sur DZONE

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Building a Secure Mobile App in the Cloud

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This is an article from DZone’s 2022 Enterprise Application Security Trend Report.

For more:

Read the Report

Building secure mobile applications is a difficult process, especially in the cloud. We must consider that mobile platforms, like iOS and Android, have completely different architectures and quality guidelines. Also, we need to take care of our cloud architecture on the back end. In this article, we will have a look at the top six security vulnerabilities, OWASP’s best practices for building/testing iOS and Android applications, and guidelines for iOS and Android. Last but not least, we will explore an example of DevSecOps for mobile applications. 

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Creating Homebrew Formulas With GoReleaser

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We chose to use GoReleaser at ObservIQ for our distro of the OpenTelemetry Collector to simplify how we build and support many operating systems and architectures. GoReleaser enables us to build targeting a matrix of GOOS and GOARCH  targets as well as automate creating a wide range of deliverables. The ones we have utilized are building tarballs, nfpm packages, docker images, and Homebrew formula.

For this article, the focus is on the Homebrew Taps capabilities in GoReleaser and our journey using it. Our goal was to make it easy for users to install our software on macOS so that they could easily try it out. We went with Homebrew as it’s familiar to many macOS users and would allow a user to try out our software and remove it just as easily when they are finished.

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Advancements in Cloud-Native and Kubernetes Observability

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This is an article from DZone’s 2022 Kubernetes in the Enterprise Trend Report.

For more:

Read the Report

In today’s world, it’s more important than ever to have visibility into your system’s performance and health. Modern applications rely on complex microservices architectures and cloud-native technologies, like Kubernetes. Observability helps us understand not just application behavior, but also infrastructure configuration changes and dependencies, as they happen in real-time. 

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Observability Maturity Model

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Modern systems and applications span numerous architectures and technologies — they are also becoming increasingly more dynamic, distributed, and modular in nature. In order to support the availability and performance of their systems, IT operations and SRE teams need advanced monitoring capabilities. This Refcard reviews the four distinct levels of observability maturity, key functionality at each stage, and next steps organizations should take to enhance their monitoring practices.
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Reverse Pull-Requests

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In a fast-paced world, more teams have microservices architectures and are making the shift to Continuous Deployment and Trunk-Based Development. For one of our client’s teams, that meant no feature branches, pairs always committing to main, pushing frequently (multiple times per hour, as often as every 1–4 commits) and those changes landing in production 20–30 minutes later.

With pair programming, no feature branches, and such continuous change, code reviews would seem redundant or extremely difficult with little in the way of tooling support. How on earth would you use GitHub’s Pull Request review features in this setting when there’s no feature branch to diff?

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Using PGBouncer With CockroachDB Serverless

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Given CockroachDB scales with vCPU, there’s a hard limit to how many active connections we can support per vCPU before a serious problem arises. PGBouncer stretches the limits a bit making it a cost-effective option. In serverless architectures, there is no client-side connection pooling, and using middleware like PGBouncer can alleviate the problem of connection storms. Please see my previous articles on the topic for more details.

Previous Articles

  1. Using PGBouncer with CockroachDB
  2. Using PGBouncer with Cockroach Cloud Free Tier
  3. Exploring PGBouncer auth_type(s) with CockroachDB

Motivation

We’ve covered how to deploy PGBouncer with a self-hosted CockroachDB cluster. Today, I’m going to demonstrate how to run PGBouncer along with the Cockroach Cloud free-forever tier database. The overall concepts are identical, but we will highlight some of the major differences in deploying PGBouncer with a cloud product.

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Red Hat Summit 2022 – Talking Architecture Shop Series

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It’s that time of year again when we get the Red Hat Summit 2022 call for papers!

This year seemed to be a perfect time to go all in with sessions around our architectures based on a series of talks we’ve designed to showcase the various aspects we cover. Some are vertical aligned and others are just customer domains, but all of them include extensive research into how to implement successful architectures at scale.

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Raising the Bar on Security by Purging Credentials From the Cloud

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The Cloud is ubiquitous: any company looking to ramp up quickly will provision its compute, networking, and storage with its preferred cloud provider, and get started rolling out their products.  That makes total sense from a business perspective.  The Cloud has simplified development and automation exponentially over the years, and emerging tech such as AI and IoT will only accelerate this.  

However, the catch is that the very same foundational architectures which drive the Cloud’s efficiency, flexibility, and cost benefits ultimately also are its weakest links from a security perspective.   The result is the daily march of headlines we all read about: ever larger and deeper breaches of data and systems.

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Apache Kafka in Cybersecurity for SIEM/SOAR Modernization

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Apache Kafka became the de facto standard for processing data in motion across enterprises and industries. Cybersecurity is a key success factor across all use cases. Kafka is not just used as a backbone and source of truth for data. It also monitors, correlates, and proactively acts on events from real-time and batch data sources to detect anomalies and respond to incidents. This blog series explores use cases and architectures for Kafka in the cybersecurity space, including situational awareness, threat intelligence, forensics, air-gapped and zero trust environments, and SIEM/SOAR modernization. This post is part six: SIEM/SOAR Modernization.

Blog Series: Apache Kafka for Cybersecurity

This blog series explores why security features such as RBAC, encryption, and audit logs are only the foundation of a secure event streaming infrastructure. Learn about use cases,  architectures, and reference deployments for Kafka in the cybersecurity space:

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