Comprehensive Studies of DevOps & Cloud Tools

Immersive Learning Program

In today's fast-evolving IT landscape, mastering DevOps and Cloud Tools is crucial for accelerating software delivery, automating workflows, and managing scalable infrastructure. This 145-hours Comprehensive Studies of DevOps & Cloud Tools program by Emerging India Analytics offers hands-on training designed to equip professionals with practical expertise in Linux, Automation, Configuration Management, Containerization, Cloud Computing, and CI/CD pipelines. Participants will work with industry-standard tools including Terraform for infrastructure as code (IaC), Puppet for automated configuration management, and Jenkins for continuous integration and delivery. Through real-world projects and guided exercises, learners gain the skills to deploy and manage applications efficiently on cloud platforms like AWS, and utilize orchestration tools such as Kubernetes for resilient and scalable environments.

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Introduction

Comprehensive Studies of DevOps & Cloud Tools

This 145-hours intensive program Comprehensive Studies of DevOps & Cloud Tools, equips learners with job-ready skills in modern IT infrastructure and DevOps. It begins with foundational training in Linux and Red Hat system administration, covering command-line operations, user and file management, and basic networking. Learners then dive into configuration management with Ansible and Puppet, gaining hands-on experience automating system setups. The program also covers cloud infrastructure with AWS, focusing on core services like EC2, S3, IAM, and VPC for deploying and managing secure, scalable environments.
Containerization and orchestration are taught using Docker and Kubernetes, enabling participants to package and manage applications in production. The curriculum also includes CI/CD pipeline automation with Jenkins and GitLab, along with Infrastructure as Code (IaC) using Terraform. Capstone projects reinforce learning by simulating real-world DevOps scenarios—such as deploying serverless applications, creating automated pipelines, and managing cloud resources—ensuring learners graduate with practical, industry-aligned experience

Comprehensive Studies of DevOps & Cloud Tools

Tools

Python
Linux
RedHat
Ansible
AWS
Docker
Kubernetes
Terraform
Puppet
CI/CD
Jenkins
Maven

Program Structure

30-Hours Pre-Learning Module

Before live sessions start, learners will access recorded tutorials covering Linux basics, DevOps culture and philosophy, introductory Python scripting, and an overview of cloud computing. This pre-learning phase ensures participants are ready for hands-on, instructor-led training.

145-Hours Live Instructor-Led Program Training

This includes detailed, practical sessions on Linux administration, Red Hat, AWS, Ansible, Docker, Kubernetes, Terraform, Puppet, and CI/CD with Jenkins & GitLab to build automation, orchestration, and deployment skills for real-world DevOps environments.

Access to Recorded Live Videos

Learning does not stop here. To support better understanding of concepts and skill mastery, recorded videos of live classes will be provided to learners. These videos will be accessible for up to 6 months after course completion.

Specialized Projects & Assignments

To master the skills acquired during the course, learners are required to complete and submit a few projects within one month of course completion. Expert trainers will be available during this period to provide guidance, support, and clarification when needed.

Curriculum

LEARN WITH A WORLD CLASS CURRICULUM

Module 1. Course Introduction
Day 01: Introduction to DevOps, Scope of DevOps
  • DevOps fundamentals and principles
  • DevOps lifecycle and methodology
  • DevOps tools overview
  • Benefits and challenges of DevOps
Module 2. Fundamentals of Linux
Day 02: Introduction to Linux, Linux Distribution, Types of shell, Package Installation, Basic Linux Commands, Shell scripting
  • Linux architecture and distributions
  • Command line interface and shell types
  • Package management systems
  • Essential Linux commands
  • Shell scripting basics
Day 03: Core Linux Components: I/O Hardware (Udev), Memory management, File Management system (Storage, NFS etc), Package Management
  • I/O hardware management with Udev
  • Linux memory management
  • File systems and storage management
  • NFS and shared storage
  • Advanced package management
Day 04: Process Management, Linux Security: Permission, ACLs, NACLs, Server Hardening, Auditing, Multithreading
  • Process management and monitoring
  • Linux permissions (read, write, execute)
  • Access Control Lists (ACLs)
  • Network ACLs and firewall configuration
  • Server hardening techniques
  • System auditing and compliance
  • Multithreading concepts
Day 05: Core Networking: SNAT, DNAT, IP, Netmask, DHCP, DNS, Virtual memory, Basic and Advanced Linux Command
  • Source and Destination NAT
  • IP addressing and subnetting
  • DHCP and DNS configuration
  • Virtual memory management
  • Advanced Linux commands
  • Network troubleshooting
Day 06: Performance Tuning: Tuning system profiles, Sysctl parameters
  • System performance optimization
  • Performance monitoring tools
  • System profile configuration
  • Sysctl parameter tuning
  • Resource management
Module 3. Fundamentals of Red Hat
Day 07: Introduction to Red Hat Linux, File System Management, User and Group Administration
  • Red Hat Enterprise Linux overview
  • Red Hat file system structure
  • File and directory management
  • User account creation
  • Group management
  • Password policies
Day 08: Package Management with Yum, System Services, and Networking Configuration
  • Yum package manager
  • Repository management
  • System service configuration
  • Systemd service management
  • Network configuration in Red Hat
  • NetworkManager
Day 09: Networking Concepts - SNAT, DNAT, IP, Netmask, and Security and Permissions
  • Advanced networking in Red Hat
  • NAT configuration
  • IP management
  • Subnet configuration
  • Red Hat security features
  • SELinux configuration
  • Firewalld management
Day 10: System Performance Monitoring, Storage Management, and Backup and Restore
  • Performance monitoring tools
  • Storage management in Red Hat
  • Logical Volume Management (LVM)
  • Backup strategies
  • Restore procedures
  • Snapshot management
Day 11: Kernel and Module Management, Remote Access with SSH, CPU Scheduling, Job Scheduling
  • Linux kernel management
  • Kernel module administration
  • Secure Shell (SSH) configuration
  • CPU scheduling mechanisms
  • Cron job scheduling
  • At and batch scheduling
Module 4. Configuration & Automation using Ansible
Day 12: Introduction of Ansible, Installation of Ansible, Ansible Architecture & Core Concepts, Inventory: Defines managed hosts and groups, Playbooks: YAML files specifying tasks to execute on hosts
  • Ansible introduction and use cases
  • Installation and configuration
  • Ansible architecture
  • Core components
  • Inventory file structure
  • Host and group management
  • Playbook basics and YAML syntax
Day 13: Modules: Reusable code units performing specific actions (e.g., file management, service management), Roles: Reusable, modular playbooks for complex tasks
  • Ansible modules overview
  • Built-in modules for common tasks
  • File and service management modules
  • Ansible roles structure
  • Creating and organizing roles
  • Role dependencies
  • Galaxy roles
Day 14: Collections: Organized groups of roles, modules, and plugin, Control Machine Requirements, Managed Node Requirements, Inventory setup
  • Ansible collections concept
  • Using and managing collections
  • Control machine configuration
  • Managed node requirements
  • SSH key management
  • Advanced inventory configuration
  • Dynamic inventories
Day 15: Modules, Adhoc Commands, Playbook, Roles, Including & Importing Roles & Task Files, Writing a Playbook to Install & Configure Web Servers & Deploying an Application
  • Ansible ad-hoc commands
  • Advanced playbook techniques
  • Module parameters and options
  • Including and importing roles
  • Task file organization
  • Web server installation and configuration
  • Application deployment automation
Module 5. AWS
Day 16: Cloud Computing A Brief Introduction, AWS Features
  • Cloud computing concepts
  • Cloud service models (IaaS, PaaS, SaaS)
  • AWS overview and services
  • AWS management console
  • AWS CLI and SDK
Day 17: Global Infrastructure, AWS IAM, SAML, and Identities
  • AWS global infrastructure
  • Regions and availability zones
  • Identity and Access Management (IAM)
  • IAM users, groups, and roles
  • SAML integration
  • Federation and single sign-on
  • Security best practices
Day 18: Roles, Storage Services, AWS S3, and S3 Bucket
  • IAM role configuration
  • AWS storage service overview
  • Simple Storage Service (S3)
  • S3 bucket creation and management
  • S3 security and permissions
  • S3 versioning and replication
Day 19: Storage class, Lifecycle Management, Snowball, and CloudFront CDN
  • S3 storage classes
  • Storage class optimization
  • S3 lifecycle management
  • Snowball data transfer
  • CloudFront CDN architecture
  • Content delivery optimization
  • CloudFront security
Day 20: EC2, AWS EC2, EC2 Instances, EBS, AWS AMI
  • Elastic Compute Cloud (EC2) overview
  • EC2 instance types
  • EC2 instance management
  • Elastic Block Storage (EBS)
  • EBS volume types and management
  • Amazon Machine Images (AMI)
  • Custom AMI creation
Day 21: AWS Lambda, Cloudwatch EC2, and AWS BashScript
  • Serverless computing with Lambda
  • Lambda function creation
  • Event-driven architectures
  • CloudWatch monitoring for EC2
  • CloudWatch alarms and metrics
  • AWS CLI and bash scripting
  • Automation with AWS bash scripts
Module 6. Introduction to DevOps
Day 22: Introduction to DevOps, DevOps Architecture, & Life cycle, Workflow & Principle, Introduction to DevOps tools
  • DevOps methodology in depth
  • DevOps architecture components
  • DevOps lifecycle stages
  • DevOps principles and practices
  • CI/CD concepts
  • DevOps tool ecosystem
Day 23: DevOps Automation, Waterfall model, Agile Development and SDLC
  • DevOps automation strategies
  • Waterfall development model
  • Agile methodology
  • Scrum and Kanban frameworks
  • Software Development Life Cycle (SDLC)
  • DevOps integration with SDLC
Module 7. Docker
Day 24: Introduction and installation of Docker, Differentiate Docker and Virtualization [hub, container]
  • Docker fundamentals
  • Container concepts
  • Docker installation
  • Docker vs. virtualization
  • Docker Hub repository
  • Container basics
Day 25: DevOps and Docker, Docker CLI & Common Operations
  • Docker in DevOps workflows
  • Docker command line interface
  • Docker image management
  • Container lifecycle
  • Running and managing containers
  • Docker networking basics
Day 26: Containerization, and Microservices
  • Containerization principles
  • Docker image creation
  • Dockerfile syntax
  • Microservices architecture
  • Monolithic vs. microservices
  • Implementing microservices with Docker
Day 27: Configuration, and Advantages of Microservices
  • Microservices configuration
  • Configuration management
  • Benefits of microservices
  • Scalability and resilience
  • Microservices communication patterns
  • Service discovery
Day 28: Docker Architecture(Components of Docker Ecosystem), Summary, Docker engine, Policies
  • Docker architecture in depth
  • Docker daemon
  • Docker client
  • Docker engine internals
  • Container runtime
  • Security policies
  • Resource policies
Day 29: Registry, Swarm and Service, Placement and stack
  • Docker Registry setup
  • Private registry management
  • Docker Swarm introduction
  • Swarm cluster creation
  • Service deployment
  • Node placement strategies
  • Docker stack deployment
Module 8. Kubernetes
Day 30: Introduction to Container Orchestration, Kubernetes Architecture, Core Concepts, and Installation of Kubernetes
  • Container orchestration concepts
  • Kubernetes architecture
  • Control plane components
  • Node components
  • Kubernetes installation methods
  • Kubernetes cluster setup
Day 31: Kubernetes container, Kubernetes controller, Self-healing application
  • Kubernetes pods
  • Container configuration
  • Kubernetes controllers
  • Deployments, ReplicaSets, StatefulSets
  • Self-healing mechanisms
  • Health checks and probes
Day 32: Horizontal pod autoscaling, Persistent volume and auto-scaling
  • Horizontal Pod Autoscaler (HPA)
  • Scaling applications
  • Metrics-based scaling
  • Persistent Volumes (PV)
  • Persistent Volume Claims (PVC)
  • Storage Classes
Day 33: Alternate ways of Deploying Kubernetes, Services in Kubernetes
  • Kubernetes deployment options
  • Managed Kubernetes services
  • Service resource types
  • ClusterIP, NodePort, LoadBalancer
  • External and internal services
  • Service discovery
Day 34: Creating a Deployment in Kubernetes using YAML
  • YAML fundamentals
  • Kubernetes manifest structure
  • Deployment YAML configuration
  • Resource management
  • Labels and selectors
  • Deployment strategies
Day 35: Volumes & its Types, Secrets & ConfigMaps, Kubernetes Monitoring using Kubernetes Dashboard
  • Kubernetes volume types
  • EmptyDir, HostPath
  • Secrets management
  • ConfigMaps for configuration
  • Kubernetes Dashboard installation
  • Monitoring and visualization
  • Resource utilization tracking
Module 9. Capstone Projects: AWS WEB SERVICE
Day 36: Build a serverless web application using AWS Lambda and API Gateway. Create a static website using AWS S3 and Cloud Front. Set up a continuous deployment pipeline using AWS Code Pipeline and Code Deploy. Serverless Computing with AWS Lambda and Ansible
  • Serverless application architecture
  • Lambda function implementation
  • API Gateway configuration
  • S3 static website hosting
  • CloudFront distribution setup
  • CI/CD pipeline creation
  • AWS CodePipeline configuration
  • AWS CodeDeploy implementation
  • Integrating Ansible with AWS Lambda
  • Serverless infrastructure automation
Module 10. Terraform
Day 37: Introduction, Terraform lifecycle, Infrastructure as a Code(IaC)
  • Terraform fundamentals
  • Infrastructure as code concepts
  • Terraform lifecycle
  • Terraform benefits
  • Terraform vs other IaC tools
Day 38: IaC vs Configuration Management, Basic operations in Terraform, Terraform Code Basics, Terraform init
  • Comparing IaC and configuration management
  • Terraform workflow
  • Basic Terraform commands
  • HCL syntax basics
  • Terraform initialization
  • Providers and resources
Day 39: Plan, Apply, Deploying an end-to-end Architecture on AWS
  • Terraform plan command
  • Terraform apply command
  • State management
  • AWS provider configuration
  • Building AWS infrastructure
  • Multi-tier architecture deployment
  • Infrastructure modules
Module 11. Puppet
Day 40: Need for Configuration management, Configuration Management Tools, Puppet architecture
  • Configuration management principles
  • Configuration management tools comparison
  • Puppet overview
  • Puppet architecture components
  • Puppet Enterprise vs Open Source
Day 41: Setting up Master Slave using Puppet, Manifests & Modules, Applying Configuration using Puppet
  • Puppet master-agent setup
  • Agent certificate management
  • Manifest file structure
  • Puppet modules
  • Resource types and providers
  • Applying configurations
Day 42: Puppet File Server, Deploying Sample Software Online
  • Puppet file server configuration
  • File resource management
  • Software deployment automation
  • Package management with Puppet
  • Service management
  • Puppet modules for application deployment
Module 12. CI/CD Pipelines
Day 43: Introduction to CI/CD Pipeline, Setting up a basic CI/CD Pipeline, Implementing CD Pipelines
  • CI/CD fundamentals
  • Continuous Integration concepts
  • Continuous Delivery vs Continuous Deployment
  • CI/CD pipeline components
  • Pipeline setup basics
  • Pipeline implementation strategies
Day 44: CI CD Pipeline architecture, Pipeline anatomy, Merge request, Popular CI tools, Multibranch Pipelines
  • Pipeline architectures
  • Pipeline stages and steps
  • Merge request workflows
  • CI/CD tools comparison
  • Multibranch pipeline configuration
  • Branch-specific workflows
Module 13. CI using Jenkins and Gitlab
Day 45: Pipeline as a code, Automated Testing Strategies, Introduction to CI
  • Pipeline as code concepts
  • Jenkinsfile structure
  • GitLab CI YAML configuration
  • Testing in CI pipelines
  • Unit, integration, and functional testing
  • Test automation frameworks
Day 46: Jenkins Overview, Installation, Tomcat, Git, and Maven setup
  • Jenkins architecture
  • Jenkins installation
  • Tomcat server configuration
  • Git integration
  • Maven configuration
  • Jenkins plugins
Day 47: Jenkin Integration, Jenkin job configuration, Script and Shell builds
  • Jenkins third-party integrations
  • Job configuration
  • Build triggers
  • Jenkins scripting
  • Shell build steps
  • Pipeline script development
  • Groovy scripting basics
Module 14. Capstone Projects: AWS WEB SERVICE
Day 48: Develop a chatbot using AWS Lex and Lambda. Implement a database using AWS DynamoDB and Lambda. Use infrastructure as code to manage your AWS resources and configurations
  • AWS Lex chatbot development
  • Lambda function integration with Lex
  • DynamoDB database setup
  • Lambda function CRUD operations
  • Infrastructure as code implementation
  • AWS resource management
  • Project planning and execution
  • End-to-end deployment
  • Documentation and presentation

Skills You Will Possess

Programming & Automation Scripting
Command-Line & System Administration
Enterprise Linux & Certification Skills
IT Automation & Config Management
Cloud Deployment & Service Management
Containerization & App Portability
Container Orchestration & Scaling
Continuous Integration & Continuous Deployment
Pipeline Automation & Build Management
Configuration Management & Automation
Infrastructure as Code (IaC) Deployment

Program Benefits

Cutting Edge Curriculum

Hand crafted Course content made by Experts from various Industries. Learn through Practical case studies and multiple projects.

On the Go Learning

Online accessible E-learning Material, live interactive lectures, Industrial Graded Projects, Case Studies and Multiple Tests & Evaluations.

Build Solid Foundation

You will get 110 hours of live instructor-led lectures on the most in-demand DevOps tools.

Industry Mentorship

Receive one-on-one guidance from industry experts and confidently begin your career in the field of DevOps.

Recognized Certification

Earn a foundational in-house certificate that leads to the Government of India-approved and globally recognized NASSCOM certification.

Industry Certificate

Begin your journey toward the prestigious AI - DevOps Engineer certification (NSQF Level 5) from SSC NASSCOM, a top industry credential.

Course Certificates

Upon successful completion of the program and passing the final assessment, you will receive:

  • Course Completion Certificate from Emerging India Analytics
  • NASSCOM IT-ITeS Sector Skill Council Certification
  • Begin your journey toward the prestigious SSC NASSCOM AI-DevOps Engineer Certification, a top industry-recognized credential.

These certifications are recognized by employers globally and validate your expertise in DevOps.

Sample Certificate
Sample Certificate

Real World Projects

Projects will be a part of your Comprehensive Studies of DevOps & Cloud Tools to solidify your learning. They ensure you have real-world experience in DevOps.

Practice 20+ Essential Tools

Designed by Industry Experts

Get Real-world Experience

Beginner

Serverless Web App using AWS Lambda + API Gateway

Students will build a basic serverless backend using AWS Lambda and API Gateway to process form submissions (e.g., feedback handler). The project introduces API development without managing servers and teaches how to trigger Lambda functions via HTTP requests.

Intermediate

Serverless Automation with Lambda + Ansible

Students will automate the deployment and configuration of AWS Lambda functions using Ansible playbooks and roles. This project builds a deeper understanding of Infrastructure as Code (IaC), cloud automation, and scalable serverless resource management.

Advanced

Infrastructure as Code with Ansible on AWS

Students will define and manage AWS infrastructure — including EC2, S3, and IAM — using Ansible playbooks or Terraform. This project builds hands-on expertise in infrastructure provisioning, automation, and best practices for repeatable cloud deployments.

Career Services By emergingindiagroup

Soft Skills

Learners will be closely mentored to develop key soft skills like communication, teamwork, and adaptability, enhancing their career path.

Interview Preparation

Participate in mock interviews and receive detailed feedback sessions with experienced industry experts.

Profile Building

Attend resume workshops and get your LinkedIn profile optimized for better professional visibility.

Placement Assistance

Placement opportunities become available upon clearing the Placement Readiness Test and meeting eligibility criteria.

Exclusive access

Get exclusive access to our dedicated job portal to apply for open positions. Partnering with a select few start-ups and product companies, we offer personalized mentorship and support to help you explore relevant job opportunities and advance your career.

Real World Projects

Projects will be a part of your Comprehensive Studies of DevOps & Cloud Tools to solidify your learning. They ensure you have real-world experience in DevOps.

  • Practice 20+ Essential Tools
  • Designed by Industry Experts
  • Get Real-world Experience

Our Alumni Works At

Learners thought about us

"
It was a great experience with Emerging India Analytics. The course format and content was very good. The faculty, Ms Lakshmi is very knowledgeable. She know the subject very well and the way she conducted the sessions was very much satisfactory. Thank you so much for your services and wish you all the best. God Bless.
Yogesh Ranjan Ghavnalkar

Yogesh Ranjan Ghavnalkar

Learner

"
As a non-IT background student, I am very much satisfied with the live sessions/classes conducted by Emerging India Analytics. Special thanks to the instructor/trainer, the way he is teaching, from the basic fundamentals, that a student having zero knowledge in IT/CS & coding, can easily understand the subjects/topics.
Tushar Kanta Behera

Tushar Kanta Behera

Learner

"
Classes are progressing smoothly, doubts are consistently addressed, fostering a clear understanding. Positive atmosphere, engaged learning, and effective communication contribute to a successful academic experience.
Aadi Bhardwaj

Aadi Bhardwaj

Learner

"
Coming from non-IT background was initially worrisome but I took the bold step into this course. The tutors have been fantastic as well as the personal support team. Looking back at the journey so far, I will say it's worth the all-round commitment and I recommend this program without reservation.
Israel Samuel

Israel Samuel

Learner

Admission Details

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

1

Submit Application

Tell us a bit about yourself and why you want to join this program

2

Application Review

An admission panel will shortlist candidates based on their application

3

Admission

Selected candidates will be notified within 1week.

Program Fees

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Total Admission Fees
₹50,000*
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FAQs

1. What is the duration of the Post Graduate Program In Artificial Intelligence And Data Science?
The Post Graduate Program in Artificial Intelligence and Data Science is an intensive program that spans approximately 12 months. It includes 80 hours of pre-learning, 380 hours of core program content, 380 hours of post-program support, and 180 hours of project engagement. The program is designed to provide comprehensive training in all aspects of data science and AI.
2. What topics are covered in the course?
The curriculum is comprehensive and covers a wide range of topics including Advanced Excel, Python, R Programming, Statistics, Machine Learning, Deep Learning, Time Series Analysis, SQL, Business Intelligence tools (Tableau and Power BI), Big Data technologies (MongoDB, Hadoop, and Spark), Generative AI, Natural Language Processing, Computer Vision, YOLO, Reinforcement Learning, and Data Structures and Algorithms. The program also includes numerous practical projects and case studies.
3. Do I need any prior knowledge to enroll in this course?
No, you don't need prior knowledge in data science or programming to enroll in this course. The program is designed to start from the basics and gradually build up to advanced concepts. We have many successful learners from non-IT backgrounds. The pre-learning module will help you build a strong foundation before diving into more complex topics. However, basic computer literacy and logical thinking abilities are beneficial.

FAQs

1. What is the duration of the DevOps and Cloud Computing certification program?
The program duration is approximately 6 months, including training, hands-on labs, and project work.
2. What skills will I develop through this program?
You’ll learn Linux, AWS, Docker, Kubernetes, CI/CD pipelines, Jenkins, Git, Ansible, Terraform, and more.
3. Will I receive practical experience in DevOps and cloud tools and techniques?
Yes, the course includes hands-on labs, real-time projects, and tool-based implementation.
4. How will this program help in my career?
It prepares you for roles like DevOps Engineer, Cloud Engineer, or Release Manager with strong job demand.
5. Is certification provided upon completion?
Yes, you will receive a course completion certificate recognized in the industry.
6. Can I pursue this program alongside my current job or studies?
Yes, the program is flexible and can be pursued alongside work or academics.
7. What support is available if I have questions or need assistance during the course?
You get access to expert mentorship, 24/7 doubt support, and learning resources.
8. Are there opportunities for practical projects or internships?
Yes, the program offers capstone projects and may include internship guidance.
9. How does this program compare with others in the DevOps field?
It combines cloud computing and DevOps, covers key industry tools, and is job-oriented with real-world practice.
10. What are the future prospects after completing this program?
You can start a career as a DevOps Engineer, Site Reliability Engineer, or Cloud Solutions Architect.