• What is DevOps
  DevOps is a culture and practice that combines software development (Dev) and IT operations (Ops) to deliver software faster, more reliably, and with continuous improvement.

• Why DevOps
  It reduces development time, improves collaboration between teams, automates processes, and ensures faster delivery of high-quality software.

• Evolution of Software Development
  Software practices evolved from slow, manual processes to faster, automated, and collaborative approaches for quicker releases.

• Traditional Waterfall Model
  A step-by-step process where each phase (design, development, testing, deployment) happens in sequence. It is rigid and slow to adapt to changes.

• Agile Methodology
  Focuses on flexibility, collaboration, and iterative development—software is built and improved in small, quick cycles.

• DevOps Lifecycle
  Covers the entire process: planning, coding, building, testing, releasing, deploying, operating, and monitoring—with continuous feedback at every stage.

• What is a Git Repository
  A Git repository is a storage space where all your project files and version history are saved. It can be local (on your computer) or remote (like GitHub).

• Git Workflow
  The process of working with Git—clone a repo, make changes, stage files, commit, and push updates to share with others.

• Git Configuration
  Setting up Git with your name, email, and preferences so that commits are properly tracked.

• Git Branching
  Create separate branches to work on new features or fixes without affecting the main project.

• Git Overview
  Git is a version control system that tracks changes in code, supports teamwork, and helps manage projects efficiently.

• Git Commit
  A commit saves your changes in the repository with a message, creating a checkpoint you can return to later.

• Build Tool
  A build tool automates tasks like compiling code, packaging files, running tests, and deploying applications.

• POM (Project Object Model)
  A POM file in Maven defines the project’s configuration—dependencies, plugins, and build settings.

• Maven Lifecycle
  Maven follows a structured lifecycle with phases like validate, compile, test, package, install, and deploy to manage project builds efficiently.

• Continuous Integration (CI)
  A practice where code changes are automatically built, tested, and integrated frequently to detect issues early.

• What is Jenkins
  Jenkins is an open-source automation tool that helps in building, testing, and deploying applications using CI/CD.

• Jenkins Installation
  Install Jenkins on your system or server to start automating builds and deployments.

• Configuration of Jenkins
  Set up Jenkins with users, security, plugins, and project-specific settings for smooth operation.

• Jenkins Pipeline
  A pipeline automates the software lifecycle (build, test, deploy) using code scripts for continuous delivery.

• Job
  A Jenkins job defines a specific task like building a project, running tests, or deploying applications.

• Plugins
  Extend Jenkins functionality with plugins for tools like Git, Docker, Maven, or Slack.

• Two-Server Deployment
  Use Jenkins to deploy applications across multiple servers for scalability and reliability.

• Deployment
  Automate application delivery to servers or cloud environments with Jenkins.

• Tomcat
  Integrate Jenkins with Apache Tomcat to deploy Java-based web applications directly.

• CI/CD Pipeline
  A complete process that combines Continuous Integration (CI) and Continuous Deployment (CD) for faster releases.

• Groovy Script
  Use Groovy in Jenkins pipelines to customize and automate advanced workflows.

 What is Docker?

• Docker is a containerization tool
• Runs apps with dependencies in lightweight containers
• Ensures apps work the same everywhere

 Development / Deployment

• Development: build and test inside containers to avoid environment issues
• Deployment: move the same container to servers or the cloud for consistent performance

 VM vs Docker

• VM: needs full OS, heavy, slow
• Docker: shares host OS, lightweight, fast, uses fewer resources

 Docker Engine

• Core service that powers Docker
• Includes: Docker Daemon (background), REST API (communication), CLI (commands)

 Docker Daemon

• Background process
• Builds and runs containers
• Manages images, volumes, and networks

 Create Images

• Image = blueprint of a container
• Built using Dockerfile (setup instructions)
• Command: docker build -t myapp.

 Create Container

• Container = running instance of an image
• Lightweight, isolated, portable
• Command: docker run -d -p 8080:80 myapp

 Docker Workflow

1. Write Dockerfile
2. Build Image (docker build)
3. Run Container (docker run)
4. Test locally
5. Deploy on server/cloud
6. Share via Docker Hub/Registry

 Introduction

• Kubernetes (K8s) is a container orchestration platform.
• It helps manage, scale, and deploy containerized applications automatically.

 Container Management

• Handles multiple containers across different machines.
• Ensures apps run reliably with features like auto-healing, scaling, and load balancing.

 Kubernetes Workflow

1. The developer writes code and packages it in a Docker container.
2. The container image is pushed to a registry.
3. Kubernetes pulls the image and deploys it on worker nodes.
4. K8s manages scaling, health checks, and networking automatically.

 Kubernetes Configuration

• Apps are defined using YAML/JSON files.
• Configuration specifies containers, resources (CPU, memory), networking, and scaling rules.
• Example: Deployment files, Service files, ConfigMaps, Secrets.

 Pods

• The smallest deployable unit in Kubernetes.
• A Pod can run one or multiple containers that share storage and network.

 Containers

• Containers run the actual application inside Pods.
• They are lightweight, portable, and isolated environments.

 Orchestration

• Orchestration = automated management of containers.
• Kubernetes orchestrates:
  • Deployment (launching containers)
  • Scaling (adding/removing containers)
  • Networking (service discovery, load balancing)
  • Healing (restart failed containers automatically)

Terraform

• Terraform is an Infrastructure as Code (IaC) tool.
• It helps automate the creation, management, and updating of cloud infrastructure.

Configuration

• Infrastructure setup is defined in configuration files.
• Written in HCL (HashiCorp Configuration Language).
• Describes what resources should exist (not how to create them).

Files

• Main configuration is usually in main.tf.
• Other files:
  • Variables.tf → define input values
  • outputs.tf → define outputs after deployment
  • terraform.tfstate → stores current infrastructure state

Providers

• Providers connect Terraform to cloud platforms or services.
• Examples: AWS, Azure, GCP, Kubernetes.
• Each provider manages specific resource types.

Resources

• Resources are the actual infrastructure elements.
• Examples: VM instances, networks, storage, and databases.
• Defined in configuration files with desired settings.

Variables

• Used to make Terraform code flexible and reusable.
• Can define defaults or pass values during runtime.
• Example: region, instance type, or project name.

Introduction

• Ansible is an open-source automation tool.
• Used for configuration management, application deployment, and IT orchestration.
• Agentless → only needs SSH to connect to systems.

Configuration

• Ansible configurations are written in YAML files.
• Define how servers, apps, and environments should be set up.

Ansible

• Core tool that handles automation tasks.
• Executes instructions from playbooks on target systems.

Playbooks

• Playbooks are YAML files with automation tasks.
• Define what actions to run on which hosts.
• Example: installing software, updating configs.

Inventory

• A file that lists hosts/servers where Ansible will run.
• Can be static (list of IPs/hostnames) or dynamic (cloud-based).

Ansible Modules

• Pre-built units of work in Ansible.
• Each module performs a specific task (e.g., install a package, copy a file).
• Example: yum, apt, copy, service.

Ansible Roles

• Help organize playbooks into reusable components.
• Each role has tasks, variables, templates, and files.
• Makes large projects easier to manage.

Command Line Interface (CLI)

• Ansible commands are executed through the CLI.
• Examples:
  • ansible all -m ping → test connection to all hosts
  • ansible-playbook site.yml → run a playbook

Splunk Indexer

• The indexer stores and organizes incoming machine data.
• It converts raw data into searchable events.
• Supports fast search, filtering, and reporting.

Splunk Search Head

• The search head is the web interface where users run searches.
• It distributes search requests to indexers and displays results.
• Provides dashboards, reports, and visualizations.

Splunk

• Splunk is a data analytics and monitoring platform.
• Collects, indexes, and analyzes machine-generated data (logs, metrics, events).
• Helps in troubleshooting, security monitoring, and business insights.

Forwarding and Receiving

• Forwarders collect data from source machines and send it to indexers.
• Receiving happens at the indexer end, where the data is ingested.
• Ensures real-time, reliable data transfer from multiple sources.

Prometheus Server

• The core component of Prometheus.
• Collects, stores, and processes monitoring data.
• Scrapes metrics from configured targets at regular intervals.

Prometheus Time-Series

• Prometheus stores data as a time series (values changing over time).
• Each time series has:
  • A metric name (e.g., http_requests_total)
  • Labels (key-value pairs for filtering, e.g., method="GET")
  • A timestamp with the value.
• This allows detailed monitoring and analysis of system performance.

Prometheus

• Prometheus is an open-source monitoring and alerting system.
• Designed for reliability and scalability.
• Works well for cloud-native and containerized environments.
• Supports alerting through Alertmanager and visualizations via Grafana.

Grafana

• Grafana is an open-source visualization and monitoring tool.
• Used to analyze and display metrics from various data sources.
• Popular for real-time dashboards and system monitoring.

Dashboard

• A dashboard is a collection of visual panels.
• Displays metrics in graphs, charts, and tables.
• Helps teams track system health, performance, and trends at a glance.

Data Sources

• Grafana connects to different data sources like Prometheus, InfluxDB, Elasticsearch, MySQL, and more.
• Data sources provide the metrics that Grafana visualizes.
• Multiple sources can be combined in one dashboard.

Alerts

• Alerts notify users when certain conditions are met (e.g., CPU usage > 80%).
• Can be sent via email, Slack, PagerDuty, etc.
• Ensures quick response to critical system issues.