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Terraform: Steps to create complete AWS S3 module

Terraform enables users to simplify the process of organizing and reusing infrastructure code by creating modules.
Using Terraform modules becomes important in cases where users need to reuse infrastructure code across different projects.
Step 1 involves setting up the module structure to keep the Terraform code organized and maintainable.
Step 2 involves defining the core resources for the module in the main.tf file.
Step 3 requires defining the input variables for the module in the variables.tf file.
Step 4 involves defining the required providers and their respective versions in providers.tf.
Step 5 involves defining the output values that will be exposed by the module in outputs.tf file.
Step 6 involves using the Terraform module in the root configuration by defining the values for the variables.
Creating a Terraform module helps organize code and define reusable components, such as managing S3 buckets with advanced features like versioning, encryption, and policies.
Terraform modules can be used to minimize duplication, encapsulate logic for a group of resources, and follow best practices.

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Jenkins-out CircleCI-in : Does It Worth To Switch Over ?

Jenkins has been widely used for CI/CD processes for many teams and projects.
Migrating from Jenkins to a more modern CI/CD tool like CircleCI can be beneficial.
CircleCI allows local installation of its CLI for debugging and iterating on pipelines.
CircleCI also allows running jobs locally, providing convenience and ease of use.

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Medium

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Engineering’s Business Value: From Black Box to Clarity

Technology has traditionally been viewed as a cost center and impacted by layoffs and reduction-in-force initiatives, but Value Stream Management (VSM) practices can address the challenge of linking and communicating the ROI of engineering investments.
VSM aligns work with value streams that deliver measurable business and customer outcomes for accurate tracking of costs, ROI, and value delivery at the team and product level.
Modern organizations are solving cost reduction challenges through intentional team design, using stable, cross-functional teams with dedicated software engineers and selective sharing of specialized roles across a limited number of teams.
Defining clear, measurable outcomes and improving visibility can help establish frameworks that articulate the tangible value technology brings to the business, address unnecessary overhead, and avoid layoffs caused by poor resource planning.
Technology success stems from excelling in two core areas: flow and realization. Structured OKRs can bridge these areas by translating organizational strategy into team-level objectives and ensuring every technical effort connects directly to business outcomes.
Success lies in whether work delivered creates valuable outcomes and the ability to gain insights even when results fall short of expectations, empowering teams to refine their approach or pivot entirely.
Starting with anticipated outcomes enables teams to develop meaningful OKRs that align with broader strategic objectives, define clear customer-centric goals and connect directly to the organization’s strategic direction.
Measurable business features and technical debt investments, when linked to specific outcomes, become strategic initiatives with identifiable business value, rather than “mysterious maintenance work”.
By documenting both anticipated and actual outcomes at the epic level, teams can refine their approach and make strategic decisions that drive growth, while justifying investments and creating a roadmap for long-term success.
By closing the gap between investment and impact, organizations can transform how they view technology, from a cost center to a catalyst for innovation, growth, and customer satisfaction.

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Medium

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Profitable Engineering: Linking Software Engineering to Business Results

This three-part series aims to show a direct connection between software engineering and business results.
It provides practical insights and strategies to shift the perspective of technology as a cost center.
The articles cover topics such as redefining the value of technology, evolving engineering as a strategic partner, and shifting towards delivering meaningful outcomes.
The series encourages readers to transform their approach and align technology with measurable outcomes.

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Dev

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The Linux Boot Process: A Detailed Walkthrough

The Linux boot process is a systematic sequence of steps that prepares the operating system for user interaction.
The process includes power-on and system initialization, POST, bootloader execution, kernel initialization, starting the init system, service and target initialization, reaching the login prompt.
BIOS and UEFI are firmware interfaces used for system initialization.
GRUB is the commonly used bootloader in Linux systems, responsible for loading the kernel.

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Dev

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🚀Go-ing Beyond Basics: A Beginner's Dive into Go Programming for DevOps

Go (Golang) is a programming language gaining popularity in the DevOps domain.
Key reasons to choose Go include static typing, fast compilation, built-in concurrency, and extensive libraries.
The process of setting up a Go module involves initializing it and understanding packages.
Go provides various ways to declare and manage variables, including variable declaration, data types, type inference, shorthand declaration, and default values.

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Automate EC2 Deployments on AWS with Terraform Modules

Terraform is an infrastructure-as-code tool created by Hashicorp to write infrastructure configurations in declarative code.
Terraform can manage components such as storage, computing, networking, DNS entries, and the security of your applications.
Terraform workflow has three core stages: write, plan, and apply.
Terraform modules allow you to organize all related resources into reusable packages by grouping them into specific .tf files.
Terraform modules make programmatic infrastructure management easier, they are perfect for large-scale and complex infrastructure deployment.
In this article, we explained how Terraform modules help our infrastructure code become scalable and reusable, especially in complex infrastructure setups.
We also wrapped it up by creating an ec2 instance using Terraform modules on AWS.
A Terraform modules project should have root module, child modules, and published modules(optional).
Terraform workflow involves three steps: write, plan and apply.
Modules in Terraform allow you to organize all related resources into reusable packages by grouping them into specific .tf files.

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Dev

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Forward Proxy vs Reverse Proxy vs Load Balancers

Forward proxies, reverse proxies, and load balancers are part of the backbone of modern web infrastructure and help organizations scale, secure, and optimize their systems.
A forward proxy acts as a middleman between a client and the webserver holding the desired data, while a reverse proxy manages incoming requests on behalf of backend servers.
Forward proxies can provide protection and filtering, logging, and monitoring, and cache responses.
Reverse proxies conceal the backend servers' details from clients, distribute incoming traffic across many backend servers, and offer centralized traffic management.
Load balancers distribute incoming network or application traffic evenly across many servers, ensuring no single server is overwhelmed and helps maintain performance, availability, and reliability.
Forward proxies manage outbound traffic, reverse proxies manage inbound traffic, and load balancers distribute incoming traffic among multiple servers.
Forward proxies focus on client anonymity and filtering, reverse proxies optimize and secure server traffic, and load balancers prioritize traffic distribution and server efficiency.
Forward proxies protect users, reverse proxies protect servers, and load balancers ensure reliability without a primary focus on security.
All these components complement each other in building efficient and high-performing systems when put together.
Understanding how these components work and how to use them would help optimize traffic flow, enhance security, and make web applications scalable and reliable.

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Dev

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Enforcing guardrails in the AWS environment

AWS offers its customers multiple ways to enforce guardrails – a mechanism to allow developers or DevOps teams to achieve their goals while keeping pre-defined controls.
Service control policies (SCPs) allow configuring maximum allowed permissions identities have over resources within an AWS organization.
Resource control policies (RCPs) allow configuring the maximum allowed permissions on resources within an AWS organization.
Declarative policies allow customers to centrally enforce desired configuration state for AWS services using AWS Organizations console, AWS CLI, CloudFormation templates, and AWS Control Tower.
Permission boundaries define the maximum permissions granted using identity-based policies attached to an IAM user or IAM role.
Each alternative serves a slightly different purpose for accessing resources within AWS Organizations at a large scale.
AWS does not grant any access by default - if an AWS service has not been allowed using an SCP somewhere in the AWS Organization hierarchy, no identity will be able to consume it.
Designing SCPs, RCPs, and Declarative policies as guardrails have limitations and a maximum size of 5120 and 10000 characters, respectively.
Permission boundaries also have their limitations and a maximum size of 6144 characters.
It is recommended to read AWS documentation and watch the lecture 'Security invariants: From enterprise chaos to cloud order from AWS re:Invent 2024' for better understanding.

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Dev

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SSO (Single Sign-On) and Identity Federation: A Cloud Engineer's Perspective

Single Sign-On (SSO) and Identity Federation are key concepts in modern identity management, particularly in cloud-based and hybrid IT environments.
SSO is an authentication mechanism that allows users to access multiple applications or services with a single set of credentials (e.g., username and password).
Identity Federation is the process of linking identities across multiple distinct systems or organizations, allowing users to authenticate in one domain and access resources in another without maintaining multiple sets of credentials.
SSO improves the user experience, reduces IT overhead, enhances security, and simplifies compliance.
Identity Federation enables cross-domain collaboration, centralized identity management, cloud integration, and scalability.
The initial setup complexity, single point of failure, security concerns, and interoperability issues are the challenges of implementing SSO and identity federation.
AWS SSO with Identity Federation is a common use case, requiring setting up SSO, enabling identity federation, integrating Cognito, securing the application, and testing the SSO setup.
Role-based access control and IAM roles with federated users are crucial for secure access control.
AWS Cognito is a scalable, customizable, and multi-provider authentication service that integrates directly with AWS services for secure access.

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Dev

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Kubernetes DaemonSets: Managing System-Level Components Across Every Node

Kubernetes DaemonSets are used to manage the deployment of specific pods across all or particular nodes in a cluster.
DaemonSets ensure that a copy of a particular pod is running on all nodes, which is useful for system-level components, agents, or services.
Another use is running workloads such as networking services, logging agents, monitoring agents, or services that need to interact with the node.
Key features include One pod per node dynamic scaling and node selection.
DaemonSets support rolling updates and are ideal for managing system-level services like logging, monitoring, network proxies, and security agents.
You define a DaemonSet using a YAML file that specifies the desired state of the pods.
Common use cases include cluster-level logging, monitoring, security, network proxies, and system daemons.
Best practices involve using DaemonSets for system-level components, defining resource requests and limits, considering node affinity, using taints and tolerations, and rolling updates.
DaemonSets in Kubernetes are essential for managing system-level services and ensuring that each node has a copy of the pod, providing benefits like scaling, dynamic node addition, and automatic management of system daemons.

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Dev

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Multi-Container Pods in Kubernetes: Best Practices and Use Cases

Kubernetes pods can contain more than one container that share the same network namespace and storage, and can be managed together as a unit.
Multi-container pods are beneficial when closely coupled containers need to be grouped together. For example, supporting a main application with auxiliary services or sidecar containers
Multi-container pods share the same network IP and storage volumes. Containers can communicate with each other via localhost.
Kubernetes treats all containers in a multi-container pod as a unit. This simplifies the deployment and scaling of multi-container pods.
Common multi-container pod patterns include sidecar, ambassador, adapter, and init containers.
When designing multi-container pods, it is good practice to design containers with a single responsibility, use sidecars for auxiliary functions and implement health and liveness probes.
Multi-container pods are useful when co-locating tightly coupled application components and when running auxiliary tasks such as initialization, configuration, or environment setup in init containers.
If containers in a pod do not need to share resources, it's better to deploy them in separate pods for independent scaling and easier management.
Multi-container pods simplify the management of complex Kubernetes workloads, allowing containers to share resources and improving operational efficiency.
Multi-container pods are a key component of advanced Kubernetes deployments and offer flexible solutions for enhancing application functionality or integrating with other systems.

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Kubernetes Operators: Automating Application Management with Custom Controllers

Kubernetes Operators are an advanced and automated way of managing complex, stateful applications and services on top of Kubernetes clusters.
Operators encapsulate the domain-specific knowledge of an application or service and automates its lifecycle tasks, such as deployment, scaling, backup and restore, updates, configuration changes, monitoring, and recovery.
Operators enable you to manage complex, stateful applications in a Kubernetes-native way, automating tasks that would traditionally require manual intervention or scripting.
Custom Resources (CRs) and Custom Resource Definitions (CRDs) are key concepts in Kubernetes Operators. These extensions of the Kubernetes API allow you to define new types of resources specific to your application, and enable the Kubernetes API server to understand and validate the custom objects.
Kubernetes Operators are typically implemented as controllers that interact with the Kubernetes API server. When a custom resource is created or updated, the operator's controller receives an event, compares the actual state of the resource to the desired state, and takes the appropriate action.
Operators help automate complex tasks in Kubernetes, particularly for stateful applications. They provide automation, application-specific logic, consistency, and a repeatable management process.
Helm, Ansible, and Go are often used for building Operators. The Operator SDK provides a framework for building Operators in Go, Ansible, or Helm.
Best practices for Kubernetes Operators include error handling, reconciliation, testing and validation, operator metrics and monitoring, and versioning and upgrades.
Kubernetes Operators provide a powerful and automated way to manage complex, stateful applications and services on Kubernetes, reducing the need for manual intervention, enhancing operational efficiency, and maintaining high availability and reliability of applications.

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Dev

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Managing Applications with Kubernetes Helm Charts: A Complete Guide

Helm is a package manager for Kubernetes that simplifies the deployment and management of apps within Kubernetes clusters.
A Helm Chart is a collection of files that defines a related set of Kubernetes resources and contains templates, default values, and metadata to install and run an application.
Helm provides an easy way to package, configure, and deploy applications, making it an essential tool for developers and system administrators.
Key concepts of Helm include the Helm chart, Helm release, Values, templates, and Helm repositories where charts are stored.
Helm is used for complex applications like ingress controllers, databases, and microservices.
Helm is a robust solution for managing applications at scale with the ability to install, upgrade, and roll back releases.
To use Helm, it must be installed on the user's machine using a package manager or via the GitHub releases page.
Helm repositories can be searched and added to install charts from.
Helm charts can be customized using values passed in during installation or upgrade.
Helm templates allow Kubernetes resources to be generated and customized.

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Dev

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Kubernetes Security Best Practices: Safeguarding Your Cluster and Workloads

Kubernetes Security Best Practices: Safeguarding Your Cluster and Workloads
Cluster Security: Update Kubernetes and dependencies regularly, secure the API server, use namespaces for isolation, and implement Pod Security Standards.
Network Security: Implement network policies, encrypt data in transit, and use secure container registries.
Workload Security: Use minimal container images, run containers as non-root users, limit container capabilities, and use read-only file systems.
Authentication and Authorization: Enable Role-Based Access Control (RBAC), use service accounts for applications, and employ strong authentication methods.
Monitoring and Auditing: Enable audit logging, monitor cluster activity, scan containers for vulnerabilities, and use security dashboards.

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