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Itrevolution

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Navigating Cloud Decisions: Debunking Myths and Mitigating Risks

Organizations face critical decisions when selecting cloud service providers (CSPs).
The authors challenge common assumptions about cloud vendor selection and provide practical guidance for making informed decisions that balance flexibility, cost-effectiveness, and risk mitigation.
The paper outlines significant costs and risks associated with multi-cloud approaches, particularly at the extremes of the spectrum.
To address these challenges, the authors provide guidance across several dimensions.
Tools like Wardley maps can help visualize interdependent capabilities and inform strategic cloud investments.
The cloud landscape is constantly changing, requiring organizations to adapt.
When selecting cloud providers, organizations should focus on clarifying their mission objectives, defining clear performance signals, and planning for potential exits.
Successful cloud strategies require a nuanced understanding of an organization’s specific needs, constraints, and long-term goals.
For USG projects, it’s crucial to understand constraints related to obtaining Authority to Operate (ATO).
Multi-cloud strategies, while offering potential benefits, come with significant costs and risks that must be carefully weighed.

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Amazic

216

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How Devtron addresses developer productivity challenges

Devtron, founded by Rajesh Rasdan, is addressing productivity roadblocks of developers in India's quickly expanding tech ecosystem.
India has become the world's third-largest start-up ecosystem, complicating the development process and impacting productivity.
Devtron provides a flexible platform that fits seamlessly into existing DevOps toolchains.
The platform combines multiple application features and workflows to allow developers to spend more time on new ideas and less on fixing bugs or complex setups.
Devtron's CI/CD pipelines are designed for Kubernetes, allowing for smooth deployment across multiple clusters and supporting advanced methods like image promotion.
Devtron has integrated security into the development workflow enabling developers to resolve security issues before they reach production. The platform supports rules-based compliance for specific environments.
Devtron helps companies optimize resource usage by scaling environments based on multiple consumption metrics and supporting time-based scaling of resources.
The platform integrates effortlessly into existing DevOps ecosystems, making adoption smooth and straightforward.
Devtron simplifies containerization, allowing developers to put their applications in containers with just a few clicks, eliminating the need to write complex Dockerfiles.
Tools like Devtron play a key part in boosting developer productivity in India's expanding tech startup scene, as tech spending and IT industry money continues to grow.

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Dev

266

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Understanding Zero Downtime Deployments

Zero downtime deployment (ZDD) is a method of updating software without causing any service interruptions or downtime for users.
ZDD is crucial for businesses to maintain continuous service availability, preventing revenue loss and ensuring customer satisfaction.
Common strategies for achieving ZDD include Blue-Green Deployment, Canary Deployment, and Rolling Deployment.
ZDD enhances user experience, maintains business reputation, and prevents potential revenue losses due to downtime.

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Medium

128

best practices for using azure atp in hybrid environments

Azure ATP is a cloud-based security solution designed to detect and respond to sophisticated attacks in real-time.
Azure ATP offers a flexible deployment model that seamlessly integrates with on-premises infrastructure in hybrid environments.
Best practices for utilizing Azure ATP in hybrid environments include deploying the lightweight sensor, configuring communication with the Azure ATP cloud service, customizing alert settings, and monitoring and investigating alerts.
Azure ATP complements traditional security solutions by providing advanced threat detection and response capabilities, and it offers greater compatibility with existing Microsoft products and services.

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Dev

324

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Distributed Load Testing with JMeter in Testkube

Distributed Load Testing with JMeter in Testkube
Distributed testing is executing tests concurrently from multiple machines or environments. In this section, we will discuss JMeter, a distributed load-testing tool, dig deeper into its distributed systems testing architecture, and understand the associated challenges.
Apache JMeter is an open source software for distributed, performance, and load testing of applications. In the master-slave architecture of JMeter, multiple users are simulated across various machines. While this setup seems easy, there are some challenges associated with it.
Testkube is a test orchestration and execution platform that leverages the power of Kubernetes for testing cloud-native applications. Using Testkube together with JMeter to simplify the distributed load-testing process, resulting in efficient resource allocation, horizontal scalability, and simplified orchestration of tests.
While testing cloud-native applications, automated test execution commands, issues, and results can be managed through a centralized dashboard. Testkube handles all the complexities of performing distributed tests in JMeter, reducing the resource allocation overhead and complexity in your team.
With Testkube, you can leverage Kubernetes native scaling capabilities and dynamically scale the number of slaves without changing your test scripts. The Testkube Dashboard aggregates all the test executions in real-time and helps you track them for easy monitoring and debugging.
Testkube provides a sample configuration template that you can use to get started with your JMeter test. The Configuration specifies slavecount and provides the ability to configure JMeter slaves as Kubernetes pods. The provided command runs JMeter on the slaves.
Testkube loads the JMeter Dashboard in your browser, providing a User interface with a detailed view of the Application Performance Index, Requests Summary, Statistics, etc. Testkube configures and performs this action for you, making it easier for you to gather results.
Testkube provides the ability to connect to the Testkube account using the command line. Once the context is set, you can view and run your Test Workflows. This level of automation, from creating to executing the Test Workflow, enables you to work on the test and leave the execution to Testkube.
JMeter is a commonly used tool for distributed load testing. Testkube abstracts the complexities associated with it, making the process easier to handle the network configuration, resource allocation, and processing of the artifacts. By leveraging the Kubernetes features, Testkube simplifies the process of configuring slaves and gives the power to dynamically set the number of slaves.
By using Testkube together with JMeter, you can simplify the distributed load-testing process, resulting in efficient resource allocation, horizontal scalability, and simplified orchestration of tests. This will enable you to focus on the application development and testing instead of the infrastructure.

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Dev

287

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Test Execution: A 5-Step Framework for Success

Coupling test execution to CI/CD pipelines has several drawbacks that become apparent as the complexity and scale of your application or deployment infrastructure increases.
Test execution is a specific step in the STLC which consists of several other steps such as requirements analysis, test planning, test environment setup, and test cycle closure.
Executing tests and managing execution results in a scalable and efficient manner turns out to be a complex undertaking as the number of testing tools, CI/CD systems, engineers and applications grows.
Test execution can be broken down into five steps namely Define, Trigger, Scale, Troubleshoot and Report, which can help execute tests in a way that can grow correspondingly.
Apart from the tactical approach to test execution, we can define a number of criteria that need to be assessed and planned for to scale accordingly with the needs of your team and application.
Testkube, an orchestration platform for test execution, is an example of how to execute tests in line with many, though not all, aspects listed above.
Testkube provides a grounded starting point to execute tests, provide results and aid in troubleshooting. There are both open source and cloud versions available.

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Dev

195

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Optimized AWS request-response flow | Optimized Version

An optimized AWS request-response flow can be designed with scalability, security, cost-efficiency, and reliability as primary considerations.
Route 53 directs users to the closest endpoint, reducing latency by geolocation and latency-based routing.
CloudFront caches and serves static and dynamic content globally to reduce load on backend servers.
Use AWS WAF rules to block unwanted traffic, reducing costs by filtering malicious requests early.
API Gateway manages and secures API calls, including throttling and caching.
AWS Lambda serves as an ideal option for microservices and event-driven applications to scale automatically.
Use Auto Scaling Groups (ASG) for EC2 instances to dynamically adjust capacity based on load. Leverage Spot Instances for cost-savings where possible.
Use AWS Fargate (serverless compute for containers) with ECS or EKS for microservices to avoid managing the underlying infrastructure.
Enable DynamoDB Auto Scaling to handle traffic fluctuations. Use DAX (DynamoDB Accelerator) to improve response times for read-heavy applications.
Use S3 Transfer Acceleration for faster uploads globally, especially for user-uploaded content.

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Dev

262

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AWS Architecture | Request-Response flow in a complete AWS architecture | Best Explanation

The request-response flow in a complete AWS architecture involves several key services working together.
The flow begins with Route 53 for DNS and traffic direction, followed by security with WAF & Shield.
CloudFront enhances content delivery, while VPC and subnets provide network segmentation.
The flow continues with Elastic Load Balancer for traffic distribution, EC2 instances for application logic, and data access through EFS and ElastiCache.

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Hackernoon

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The Top React Reporting Tools

Adopting a React report library can save time and effort through premade templates and intuitive report design functions.
Evaluating the pros and cons of each React reporting tool should be paramount to a solid decision.
ActiveReportsJS offers incredible flexibility without compromising convenience, boasting zero server dependency.
Stimulsoft Reports.JS is great for mid-to-large teams looking to streamline React development cycles. Much of its value stems from the depth of integration.
Jsreport is recommended for small (but experienced) teams who need to quickly incorporate compelling charts into their web apps.
Jsreports is certainly a beneficial tool if you're willing to pay the price. This library is best suited for organizations that need to automate report generation.
Bold Reports is a great library for enhancing decision-making in modern apps. Its design focus stands out among other notable features.
Any industry demanding massive data hauls, like healthcare, finance, eCommerce, and government sectors, can find value in Telerik Reports.
DevExpress Reports offers an extensive component library and endless customization features. It's especially useful for incorporating data interactivity into your web apps.
ActiveReportsJS is a cut above the rest with its vast library full of quality report components.

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Dev

104

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Week 3 in DevOps: Beginning with Advanced AWS Services and Security

This week, the focus of the DevOps journey is on exploring advanced AWS services and learning about cloud security.
Key AWS services being explored include Amazon RDS, AWS Lambda, and Amazon S3.
The importance of cloud security is emphasized, with a focus on IAM and security groups in AWS.
Early challenges include understanding the details of each AWS service, but there have been wins such as successfully creating a Lambda function and an IAM policy.

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Silicon

266

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Apple Set To Be Fined Under EU’s Tough DMA – Report

Apple is set to be fined by the EU under the tough Digital Markets Act (DMA) rules, making it the first company to be sanctioned by the Commission.
The European Commission believes Apple is in breach of DMA over its restrictions on App Store developers' communication with users.
Violation of DMA rules can lead to fines of up to 10% of a company's annual global turnover.
Apple has already faced a hefty financial penalty in Europe, including a €13 billion tax fine.

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Hashicorp

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Speed up app delivery with automated cancellation of plan-only Terraform runs

A new feature has been announced that automatically cancels plan-only Terraform runs triggered by pull requests in version control systems (VCS) for HCP Terraform and Terraform Enterprise.
This feature helps avoid backlog of multiple runs caused by new commits, speeding up the application delivery process.
Users can easily cancel unfinished runs for outdated commits by selecting the option in their organization's settings.
The enhancements aim to maximize infrastructure investments and optimize plan-only Terraform runs managed through the VCS workflow.

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Dev

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Wow, pnpm, You’re Really Fast

pnpm is a package manager that saves users space and reduces build time by handling package storage differently from npm and Yarn.
When you install a package, pnpm keeps it in a global store on your machine then creates a hard link from it instead of copying, so that there is only ever one copy of a module kept on disk.
This approach saves gigabytes of disk space and provides a leaner, faster way to manage packages, which makes it ideal for a streamlined deployment environment like Heroku.
Since May 2024, pnpm is now available in Heroku via Corepack.
To get started with pnpm on Heroku, users can enable and use it with corepack after creating a new project folder.
Comparing common commands, the usage for pnpm is fairly similar to that of npm or Yarn and should be intuitive.
Users can install dependencies, build, prune devDependencies, and cache build with pnpm, which significantly speeds up build time compared to npm.
In our test, the initial build with npm using the timing buildpack took 16 seconds, while that of pnpm only took 7 seconds.
Through its storage approach, pnpm frees up substantial disk space, reduces build time in subsequent builds, and provides a faster way to manage packages, making it an attractive package manager for developers to consider.
Moreover, with its support for Heroku, it offers a seamless path to deployment in the cloud.

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Dev

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Python for DevOps: A Comprehensive Guide from Beginner to Advanced

Python is gaining popularity in DevOps because of its adaptability, ease of use, and extensive libraries.
Python simplifies repetitive tasks, works across platforms and tools, and supports diverse libraries like boto3 for AWS, requests for API interactions.
Setting up a suitable environment is crucial for effective use of Python. Install Python from python.org, use virtual environments to avoid version conflicts, and install packages using pip.
Python’s subprocess library enables automating builds and running tests directly from scripts. Python scripts can interact with CI/CD tools via APIs or command-line utilities. Deploy applications across environments using paramiko for SSH connections.
Python can automate configuration management tasks, manage resources across environments, and handle IaC tasks like provisioning servers, managing cloud resources such as AWS.
Python can collect metrics and send alerts when system thresholds are exceeded. Python can query Prometheus for real-time metrics. Use elasticsearch-py for searching and visualising logs.
Boto3, Requests, Paramiko, Docker SDK, Flask, Prometheus Client are popular Python libraries for DevOps automation. These libraries streamline various DevOps tasks, making automation more accessible.
To ensure Python scripts are reliable and maintainable, follow best practices like using virtual environments, documenting code, implementing error handling.
Python projects in DevOps include automated backup, deployment pipeline, custom monitoring dashboard. Mastering Python is essential for DevOps engineers to enhance productivity, streamline operations, and build resilient, scalable systems.

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Sysdig

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Practical usage of Sysdig OSS

Sysdig Inspect is an open-source tool designed for container troubleshooting and security investigations
Sysdig Inspect records system call activity to a .scap file, capturing every syscall across your infrastructure
Sysdig Inspect offers a powerful user interface (UI) that simplifies navigation through the vast amount of system, network, and application activity captured in .scap files
Sysdig’s filtering system is powerful and versatile, and is designed to look for needles in a haystack
Sysdig Inspect’s versatility shines in its command-line interface (CLI), making it an essential tool for cloud environments where UIs may not always be accessible
Sysdig Inspect helps you diagnose performance bottlenecks by analyzing how processes and containers interact with system resources
By analyzing real-world syscall data from captures, security engineers can refine or design new Falco rules
Sysdig Inspect is an indispensable tool for both security and performance investigations in cloud-native environments
Its flexibility, especially through its CLI, allows for quick deployment and capture in any environment, making it a must-have for modern cloud operations
Stay tuned for more insights on how to use Sysdig Inspect in different scenarios, and don’t forget to experiment with the powerful CLI features to automate and simplify your troubleshooting process

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