What Are The Top 12 Prerequisites for Cloud Computing? Does Cloud Computing Require Coding?

Did you know that 96% of Indian companies are expected to use cloud computing frameworks by the end of 2025? Understanding the prerequisites for cloud computing helps you build scalable, secure, and future-ready systems for modern businesses.

The most important prerequisites for cloud computing include a strong grasp of networking, virtualization, Linux-based operating systems, and database management. Coding is essential, primarily in Python, Java, or Bash to automate workflows, interact with APIs, and deploy scalable applications across cloud environments. 

You’ll also need experience with DevOps practices, Agile workflows, and hands-on usage of platforms like AWS, Azure, or GCP. These prerequisites for cloud computing form the technical foundation for designing, deploying, and maintaining cloud-native systems efficiently.

Want to sharpen your cloud computing skills to make a career in modern day tech companies? upGrad’s Online Software Development Courses can equip you with tools and strategies to stay ahead. Enroll today!

What are the Prerequisites for Cloud Computing?

To work efficiently with cloud platforms, you need foundational expertise across programming, operating systems, networking, virtualization, and database management. Complementing these technical skills are deployment strategies like Agile development and DevOps, along with hands-on familiarity with cloud platforms, APIs, machine learning and AI services. 

Together, these prerequisites for cloud computing enable you to build scalable, secure, and automation-ready systems optimized for modern application lifecycles.

If you want to learn essential skills to help you understand what is cloud computing, the following courses can help you succeed.

• Master the Cloud and Lead as an Expert Cloud Engineer
• Master of Design in User Experience
• AI-Powered Full Stack Development Course by IIITB

1. Programming Languages

Programming languages are prerequisites for cloud computing because every interaction with cloud platforms, whether provisioning infrastructure or writing serverless functions, requires scripting or development skills. Most cloud services offer SDKs and APIs in multiple languages, allowing you to build, manage, and scale your workloads without relying solely on web interfaces.

• Python: Python is widely used for Infrastructure as Code (IaC), automation, and backend services. With SDKs like Boto3 (AWS) and google-cloud-python (GCP), you can create custom automation scripts, trigger cloud functions, and handle data pipelines. It also supports frameworks like Flask or FastAPI for developing serverless APIs on platforms like AWS Lambda or Azure Functions
• Java: Java is crucial for building cloud-native microservices, especially in enterprise environments. It's commonly used with Spring Boot, which simplifies dependency management and configuration in scalable applications. Java integrates seamlessly with tools like Docker, Kubernetes, and Apache Kafka, making it suitable for container orchestration and distributed data streaming in cloud setups. 
• PHP: PHP is valuable for developing and maintaining dynamic web applications that run on cloud-based platforms such as Elastic Beanstalk (AWS) or App Engine (GCP). It supports REST API development, session handling, and server-side scripting, useful when scaling CMS platforms like WordPress or Joomla with auto-scaling groups and managed databases.
• C++: While less common in high-level cloud orchestration, C++ is used in performance-intensive applications such as real-time analytics, game engines, or edge computing. It can be compiled into binaries that run on cloud virtual machines connected to cloud services like AWS Greengrass or Azure IoT Edge. 
• Ruby: Ruby powers rapid development of cloud-hosted web apps, especially via the Ruby on Rails framework. It's often deployed on Heroku, a cloud PaaS that abstracts infrastructure, making it ideal for startups building MVPs. Ruby's integration with services like PostgreSQL, Redis, and Sidekiq helps create scalable backend architectures.
• Go (Golang): Go is increasingly adopted for cloud infrastructure tooling and containerized services. It’s the language behind Kubernetes, Docker, and Terraform. You’ll find Go useful when writing plugins for CI/CD pipelines to open-source DevOps tools.

Example Scenario:

A SaaS startup in Pune uses Python and Boto3 to automate resource provisioning on AWS. They run a FastAPI microservice on AWS that fetches user data from DynamoDB and sends scheduled reports via SES, cutting manual operations by over 80%.

Code Example: 

import boto3

# Initialize S3 client
s3 = boto3.client('s3')

# Upload a file to an S3 bucket
s3.upload_file('invoice.pdf', 'business-reports-bucket', '2025/invoice.pdf')

print("File uploaded successfully.")

Output:

File uploaded successfully.

The script uses boto3 to upload a file to Amazon S3. It automates part of your reporting pipeline in cloud-based applications, reducing reliance on manual uploads and improving data consistency.

Now, let’s understand security and recovery, which are also key prerequisites for cloud computing. 

2. Security and Recovery

Security and recovery define how well your cloud architecture resists unauthorized access and restores functionality after disruptions. As key prerequisites for cloud computing, they involve implementing identity controls, encryption standards, compliance logic, and data redundancy across services. Without these controls, even highly available systems remain exposed to configuration drift, data loss, or operational downtime.

• Identity and Access Management (IAM): Use IAM policies to enforce fine-grained access to resources. Tools like AWS IAM or Azure RBAC allow role-based control over infrastructure, helping you minimize privilege exposure across your team or automation scripts.
• Encryption (at Rest and in Transit): Cloud services offer built-in encryption mechanisms such as AWS KMS or Azure Key Vault. These allow you to encrypt volumes, object storage, and database entries, ensuring that even if data is compromised, it's unreadable without authorized keys.
• Disaster Recovery Planning: Recovery strategies should define Recovery Time Objective (RTO) and Recovery Point Objective (RPO). Use backup automation tools like AWS Backup, Azure Site Recovery, or snapshots in GCP to restore system states rapidly during incidents.
• Audit Logging and Monitoring: Implement log collection and threat detection using services like AWS CloudTrail, GCP Operations Suite, or Azure Monitor. This enables proactive incident response and helps meet compliance mandates.
• Compliance Readiness: Adhere to the DPDP Act for personal data and RBI’s cybersecurity guidelines if you manage financial workloads. Cloud providers allow configuration of compliance-ready templates and audit-ready environments.
• Automated Failover and Redundancy: Deploy applications across multiple availability zones or regions. Tools like AWS Route 53 or Azure Traffic Manager can automatically shift traffic during failures to ensure uninterrupted access.

Example Scenario:

A fintech firm in Mumbai manages sensitive KYC documents in a PostgreSQL database hosted on Amazon RDS. To meet RBI compliance, you encrypt data using customer-managed keys (CMKs) via AWS KMS, enforce MFA on admin roles, and automate nightly backups to a second region.

Code example:

import boto3

# Encrypting and storing a simple message using AWS KMS
kms = boto3.client('kms')

text = b"Confidential: User PAN data"
response = kms.encrypt(
    KeyId='alias/customer-key',
    Plaintext=text
)

ciphertext_blob = response['CiphertextBlob']

print("Encrypted text (bytes):", ciphertext_blob)

Output:

Encrypted text (bytes): b'\x01\x02...\x91\xac'

The script uses AWS Key Management Service to encrypt a plaintext string. In real applications, this would be used to encrypt personally identifiable information (PII) before storing it in cloud databases, aligning with India’s data protection standards.

Also read: Data Security in Cloud Computing: Top 6 Factors To Consider

3. Networking

Networking governs how cloud resources communicate with each other and with external systems. It involves configuring IP ranges, routing traffic, securing communication channels, and ensuring low-latency access to services.

As one of the core prerequisites for cloud computing, networking ensures scalability, isolation, and high availability of cloud-based applications. Without it, provisioning secure, accessible, and performant environments becomes unmanageable.

• IP Addressing and Subnetting: Every virtual resource (VMs, containers, gateways) requires a unique IP address. Understanding CIDR blocks, subnet masks, and IP allocation strategies is essential when configuring Amazon VPC, Azure Virtual Network, or GCP VPCs.
• Firewalls and Security Groups: Firewalls and security groups define inbound/outbound traffic rules. You must know how to write and apply rules for protocols (TCP/UDP), ports, and IP ranges using services like AWS Security Groups, Azure NSGs, or GCP Firewall Rules.
• Load Balancing: Load balancers distribute incoming traffic across multiple servers to ensure availability and fault tolerance. Use AWS ELB, Azure Load Balancer, or GCP Load Balancing to manage high-volume workloads or failover across zones.
• Virtual Private Networks (VPN): VPNs provide encrypted tunnels between cloud and on-prem networks. Configure site-to-site or point-to-site VPNs using AWS VPN Gateway, Azure VPN Gateway, or Google Cloud VPN for secure hybrid access.
• DNS (Domain Name System):  DNS services route user requests to cloud resources using domain names. Configure routing policies, failover records, and subdomain mappings via AWS Route 53, Azure DNS, or Cloud DNS.
• Routing Tables and NAT: Understand how NAT gateways, IGWs, and custom routing tables control packet flow within and outside a cloud VPC. These are foundational for controlling internet access and internal service communication.

Example Scenario:

A fintech startup in Bengaluru uses AWS VPC with custom subnets and NAT gateways to isolate public and private services. You configure Elastic Load Balancing to distribute traffic across EC2 instances and implement AWS Security Groups to restrict SSH access by IP range. DNS routing is managed via Route 53, enabling dynamic subdomain configuration for microservices.

If you want to learn more about AI and ML to integrate within your networking systems, check out upGrad’s Executive Diploma in Machine Learning and AI with IIIT-B. The program will help you gather expertise on cloud computing, big data analytics, and more those are critical for enterprise-grade applications. 

4. Virtualization

Virtualization abstracts physical computing resources into software-defined environments that can be replicated, scaled, and managed independently. As one of the core prerequisites for cloud computing, it allows you to provision compute, storage, and network resources without tying them to specific physical hardware. 

Virtualization supports multi-tenancy, improves infrastructure efficiency, and lays the groundwork for containers, orchestration, and resource elasticity.

• Virtual Machines (VMs):  These are software emulations of physical systems that run complete operating systems and applications in isolated environments. VMs are provisioned on-demand using services like Amazon EC2, Azure Virtual Machines, or Google Compute Engine, often from predefined images or custom AMIs.
• Hypervisors: A hypervisor is the software layer responsible for creating and managing VMs. Type 1 hypervisors (e.g., VMware ESXi, Microsoft Hyper-V, KVM) run directly on the hardware, while Type 2 hypervisors (e.g., VirtualBox) operate on top of a host OS. Understanding how to allocate vCPUs, RAM, and IOPS is crucial for performance tuning.
• Storage Virtualization: This technique pools multiple physical storage systems into a unified logical pool. You interact with it via services like Azure Disk Storage, Amazon EBS, or Google Persistent Disks, which decouple storage management from physical devices.
• Network Virtualization: It combines and abstracts physical network resources into virtual networks. This allows segmentation using VPCs (Virtual Private Clouds), subnets, network ACLs, and firewall rules across cloud environments.
• Containerization: Unlike VMs, containers package applications and their dependencies but share the host OS kernel. Technologies like Docker and orchestration platforms like Kubernetes are built on virtualization concepts and help run scalable microservices.
• Cloud-Native Resource Provisioning: Public cloud platforms use virtualization for provisioning, but services like AWS Fargate or Azure Container Instances further abstract this layer by letting you run apps without managing VMs directly.

Example Scenario:

A retail analytics firm in Delhi uses VMware ESXi on-premises for development and testing, while production systems run on Azure Kubernetes Service (AKS). By virtualizing the entire stack, compute, storage, and network, they achieve cost savings during low-traffic months and rapid scaling during festival sales.

Code Example:

# Dockerfile to containerize a Python Flask app

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

CMD ["python", "app.py"]

Output:

Container builds successfully and runs the Flask web server on port 5000.

This Dockerfile creates a container for a Python Flask application. It contains the runtime environment and dependencies, enabling consistent deployment across virtualized infrastructure like AWS ECS, Azure AKS, or local VMs.

Let’s understand operating systems and why they are critical prerequisites for cloud computing. 

5. Operating Systems

Operating systems control how cloud resources behave at the most fundamental level. They manage system calls, kernel operations, memory allocation, user permissions, and I/O scheduling, which directly impact application performance in virtualized and containerized environments. A deep understanding of OS internals is a core prerequisite for cloud computing, especially when deploying distributed systems or troubleshooting performance bottlenecks in production.

• Linux (Ubuntu, CentOS, Red Hat): Most cloud instances run a Linux distribution due to its modularity, open-source nature, and compatibility with tools like Docker, Kubernetes, and Terraform. Kernel-level control, package management (apt, yum), and service supervision (systemd, init.d) are critical skills.
• Windows Server: Common in hybrid and enterprise workloads, Windows Server is tightly integrated with Azure and supports .NET applications, Active Directory, and Windows-based container hosting. Managing services via PowerShell and automating tasks using Windows Task Scheduler are essential skills.
• Kernel Parameters and Optimization: Modifying /etc/sysctl.conf to tune kernel settings such as TCP buffer sizes or thread limits improves the performance of network-heavy applications. Cloud providers allow custom images with pre-configured kernel parameters.
• File System Management: Understanding file system types like ext4, XFS, and NTFS, as well as managing mount points, partitions, and IOPS settings, is crucial for persistent storage in the cloud. Tools like lsblk, df, and iostat help monitor disk usage and performance.
• User and Process Management: Managing users and permissions (usermod, chmod, chown) and monitoring running processes (top, ps, nice) ensures secure, resource-efficient workloads. It's essential when using shared compute instances.
• Resource Isolation and Control Groups (cgroups): On Linux, cgroups and namespaces allow you to restrict resource usage for containers or processes, a concept behind Kubernetes pod isolation. Proficiency in configuring these directly or through container runtimes is valuable.

Example Scenario:

An Indian logistics company hosts its route optimization engine on an Ubuntu-based EC2 instance. Engineers use systemctl to manage background services, tune TCP parameters for faster network communication, and configure cron jobs for batch updates. The team's ability to manage OS-level services reduces latency during peak delivery hours.

6. Agile Development

Agile development enables continuous delivery of features and fixes by emphasizing short development cycles, fast feedback, and strong collaboration across teams. As one of the essential prerequisites for cloud computing, it complements cloud-native architectures by supporting automation, containerization, and frequent deployment. Agile methodologies ensure that cloud-based applications remain adaptable, scalable, and aligned with evolving user needs.

• Iterative Development: Agile projects deliver working software in short sprints, often ranging from 1 to 3 weeks. Each iteration allows for functionality testing, user feedback, and scope adjustments. This approach integrates well with microservices and serverless workflows in cloud deployments.
• Cross-Team Collaboration: Agile requires daily coordination between developers, testers, product managers, and DevOps engineers. Cloud-based collaboration tools like Jira, Confluence, and Slack integrations with CI pipelines make this alignment smoother across remote and hybrid teams.
• Agile Metrics and Reporting: Burn-down charts, velocity reports, and cumulative flow diagrams help track sprint progress. These metrics guide retrospective meetings and inform decisions about resource allocation and technical debt resolution.
• Adaptability to Cloud-Native Architectures: Agile teams deploy to Kubernetes clusters, use Helm charts for versioned rollouts, and follow GitOps workflows for managing infrastructure alongside application code.

Example Scenario:

A SaaS-based HRMS provider in Noida adopts Scrum to deliver biweekly updates for its payroll module. Each sprint integrates user feedback into the next release. You use GitLab CI for automated testing and Azure DevOps for deployment. This setup reduces regression bugs by 60% and accelerates new feature rollout across multi-tenant cloud environments.

If you want to gain expertise on data structures for successful DevOps operations, check out upGrad’s Data Structures & Algorithms. The 50-hour free program will help you learn arrays, blockchains, and more. 

7. Cloud Service Platforms

Cloud service platforms provide on-demand access to compute, storage, networking, databases, and application services, all abstracted from physical infrastructure. Familiarity with major platforms is a key prerequisite for cloud computing because each offers specialized tools, billing models, security frameworks, and deployment pipelines. 

Deep platform knowledge allows you to choose the right service for your workload, whether it’s serverless processing, distributed training, or real-time analytics.

• Amazon Web Services (AWS): AWS offers over 200 services, including EC2 for compute, S3 for object storage, and RDS for managed databases. It supports Infrastructure as Code (IaC) through AWS CloudFormation and scalable compute via services like AWS Lambda, ECS, and SageMaker for AI.
• Microsoft Azure: Azure tightly integrates with enterprise systems through Active Directory, Office 365, and .NET ecosystems. Key tools include Azure Resource Manager (ARM) templates, Azure ML Studio for machine learning pipelines, and Azure Kubernetes Service (AKS) for orchestrating container workloads.
• Google Cloud Platform (GCP): Known for its strengths in data engineering and AI, GCP offers services like BigQuery (serverless analytics), Vertex AI (ML operations), and GKE (Google Kubernetes Engine) for container orchestration. IAM roles and organization-level resource hierarchy enable enterprise-scale governance.
• Service Model Familiarity (IaaS, PaaS, SaaS): Understanding service models allows you to architect systems at the right abstraction level. For example, AWS EC2 (IaaS), Azure App Services (PaaS), or Google Workspace (SaaS) all solve different layers of business needs.
• Cross-Platform Competency: Indian enterprises increasingly adopt multicloud and hybrid cloud strategies. Knowing how to deploy the same containerized app on AWS ECS and GCP GKE, or use Azure Arc to manage on-prem resources, is a strategic advantage.
• Billing and Resource Optimization: Mastering tools like AWS Cost Explorer, Azure Pricing Calculator, or GCP’s Billing Reports helps you manage budgets, monitor usage, and apply cost-cutting strategies like spot instances or rightsizing recommendations.

Example Scenario:

An IT services firm in Chennai manages client workloads across AWS, Azure, and GCP. They deploy a scalable ML model using AWS SageMaker, set up Microsoft SQL Server clusters on Azure for legacy apps, and run real-time data pipelines. Their engineers switch between these platforms based on SLA requirements, tool support, and pricing efficiency.

Also Read: Top 11 AWS Certifications That Will Catapult Your Career to New Heights

Now, let’s understand the different types of cloud frameworks that are essential prerequisites for cloud computing. 

8. Different Types of Cloud

Cloud deployment models define how infrastructure and services are provisioned, managed, and accessed by organizations. Choosing the right model is a critical prerequisite for cloud computing because it impacts performance, data security, compliance, and cost structure. Public, private, and hybrid cloud models each have architectural trade-offs, and selecting the wrong type may lead to scaling limitations or regulatory risks.

Public Cloud: Public cloud resources, compute, storage, networking, are delivered by third-party providers like AWS, Azure, or GCP. These are provisioned on shared infrastructure via multi-tenant models. Public clouds are ideal for startups and SaaS products that require instant scalability and low upfront investment.

Private Cloud: Private clouds are dedicated environments either on-premises or hosted externally with exclusive access. Built using technologies like OpenStack, VMware, or Azure Stack, they offer enhanced control over infrastructure. They’re best suited for industries with strict regulatory requirements such as healthcare, banking, or government.

Hybrid Cloud: Hybrid models combine public and private cloud infrastructure, often connected through VPNs or direct links. They offer the flexibility to run secure workloads on-premise while scaling demand workloads on the public cloud. Tools like AWS Outposts, Azure Arc, and Google Anthos help unify management across hybrid environments.

Example Scenario:

A digital payments company in Gurugram uses a hybrid cloud setup. Their customer-facing application runs on AWS (public cloud) for elasticity, while sensitive transaction data is stored in a private OpenStack cloud to comply with RBI’s data localization mandate. Azure Arc is used to monitor and manage both environments through a single control plane.

9. DevOps

DevOps integrates software development and IT operations into a unified workflow focused on automation, collaboration, and rapid delivery. It plays a foundational role among the prerequisites for cloud computing by enabling continuous integration, repeatable deployments, infrastructure versioning, and proactive system monitoring. 

DevOps becomes essential for maintaining velocity and system in a cloud environment where services must scale on demand and updates need to roll out without downtime. 

• Continuous Integration (CI): CI involves automating the merging, building, and testing of code from multiple contributors. You can use tools like GitHub Actions, Git CI, or Azure DevOps to trigger builds when code is pushed to repositories, ensuring bugs are detected early in development.
• Continuous Deployment (CD): CD furthers CI by automatically deploying validated builds into staging or production environments. Using pipelines in AWS CodePipeline, ArgoCD, or Spinnaker, you can implement strategies like blue/green deployments or canary releases to maintain uptime during feature rollouts.
• Infrastructure as Code (IaC): IaC treats infrastructure configurations as version-controlled files. Tools like Terraform, AWS CloudFormation, and Pulumi let you define VPCs, subnets, auto-scaling groups, and security policies in declarative syntax, making cloud environments reproducible and auditable.
• Monitoring and Logging: Real-time observability is crucial in DevOps. Integrate tools like AWS CloudWatch, Prometheus + Grafana, or Azure Monitor to track performance metrics, log errors, and set up alerts based on thresholds or anomalies.
• Configuration Management: Tools like Ansible, Chef, or AWS Systems Manager help maintain consistency across environments by managing package installations, environment variables, and service configurations across nodes.

Example Scenario:

A digital payments company in Hyderabad uses Terraform to define its entire AWS infrastructure, including EC2, VPCs, and IAM roles. Jenkins pipelines handle CI/CD for their transaction processing service, and AWS CloudWatch tracks system health, triggering alerts for high CPU usage or dropped API requests. This setup reduces deployment time from hours to under 10 minutes and minimizes rollback risks through version-controlled infrastructure.

Code Example:

# .github/workflows/deploy.yml
name: Deploy to AWS

on:
  push:
    branches: [ "main" ]

jobs:
  build-and-deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v3

    - name: Set up Python
      uses: actions/setup-python@v4
      with:
        python-version: '3.9'

    - name: Install dependencies
      run: pip install -r requirements.txt

    - name: Run tests
      run: pytest

    - name: Deploy to AWS Lambda
      run: |
        zip function.zip lambda_function.py
        aws lambda update-function-code --function-name myLambdaFunction --zip-file fileb://function.zip

Output:

All tests passed.
Lambda function successfully updated.

This GitHub Actions workflow installs dependencies, runs tests, and deploys a Lambda function on AWS whenever code is pushed to the main branch.

10. Artificial Intelligence

Artificial Intelligence (AI) enables machines to perform tasks that typically require human cognition, like recognizing patterns, processing language, and making data-driven decisions. Within cloud environments, AI services integrate deeply with compute, storage, and data pipelines, allowing you to build intelligent systems without managing complex infrastructure. 

As one of the emerging prerequisites for cloud computing, AI helps automate decisions, personalize user experiences, and optimize backend operations through models deployed at scale.

• Machine Learning (ML): ML involves training statistical models using large datasets to make predictions or classifications. Cloud platforms like Amazon SageMaker, Azure ML Studio, and Google Vertex AI provide end-to-end pipelines from data ingestion to model deployment, abstracting the infrastructure layer.
• Natural Language Processing (NLP): NLP enables systems to understand, classify, and generate human language. You can automate feedback analysis, sentiment tagging, and conversational interfaces using Azure Text Analytics, AWS Comprehend, or Google Cloud Natural Language API.
• Computer Vision: Computer Vision models process image or video inputs to detect features, objects, faces, or events. AWS Rekognition, Azure Computer Vision, and GCP AutoML Vision provide pre-trained APIs and custom training pipelines for image classification, facial recognition, or quality assurance systems.
• AI-Powered Automation: AI enables intelligent automation of decision trees, customer support, fraud detection, and workflow optimization. These are implemented via managed services like Google Dialogflow, Azure Bot Framework, or event-driven Lambda functions tied to ML inference endpoints.
• Inference Optimization: You must understand GPU vs CPU inference, quantization techniques, and edge inference using services like AWS Inferentia, GCP Edge TPU, or Azure Percept to reduce latency and cost in production systems.

Example Scenario:

A health-tech startup in Bengaluru processes diagnostic reports using AI models trained on AWS SageMaker. You use NLP to interpret doctors’ notes via Amazon Comprehend Medical and deploy a model endpoint to classify disease likelihoods. Their system supports thousands of inferences daily, reducing manual triage time by 40%.

Let’s understand the importance of APIs for cloud computing in detail. 

11. Application Programming Interfaces (APIs)

APIs define how software components communicate across systems, enabling modular and scalable application development. In cloud environments, APIs are the backbone of service orchestration, automation, and integration. They’re a core prerequisite for cloud computing because almost every cloud service, compute, storage, ML, security, exposes functionality through REST or GraphQL APIs. Building, consuming, and securing APIs is fundamental for deploying and managing cloud-native applications.

• REST APIs (Representational State Transfer): REST is the most widely adopted standard for web APIs. It uses HTTP methods (GET, POST, PUT, DELETE) to interact with resources. Cloud services like AWS S3, GCP Cloud Functions, and Azure Storage offer REST endpoints for provisioning, querying, or modifying data.
• GraphQL: GraphQL allows clients to specify the exact structure of data they need. This improves efficiency by reducing over-fetching, which is common in REST APIs. Platforms like Hasura, Apollo Server, and Google Cloud API Gateway support GraphQL for dynamic frontends and real-time dashboards.
• Authentication and API Keys: Secure APIs using OAuth2, JWT, or API keys. Cloud providers typically require token-based auth, enforced through gateways or identity providers (e.g., AWS IAM or Azure Active Directory).
• API Gateways: Services like AWS API Gateway, Azure API Management, and GCP API Gateway help publish, throttle, and secure APIs. They also allow monitoring, versioning, and transformation of requests in multi-service environments.
• Testing and Documentation Tools: Tools like Postman, Insomnia, and Swagger (OpenAPI) help test endpoints, validate responses, and auto-generate documentation for development and compliance needs.
• Serverless API Backends: Many modern applications use serverless functions (e.g., AWS Lambda or Azure Functions) as API endpoints. This supports scalable and cost-efficient API deployment without managing infrastructure.

Example Scenario:

A ride-sharing app startup in Mumbai integrates real-time driver location tracking using REST APIs connected to GCP Firebase. The customer-facing dashboard uses GraphQL to fetch only relevant fields like ETA and driver ID from their microservices architecture, improving response times by 30% on 4G networks.

Example Code:

from flask import Flask, jsonify, request

app = Flask(__name__)

@app.route('/status', methods=['GET'])
def status():
    return jsonify({"status": "API is running", "region": "India"})

if __name__ == '__main__':
    app.run(debug=True)

Output:

GET /status → {"status": "API is running", "region": "India"}

The Flask-based REST API returns a simple health check response. Such endpoints are standard in cloud apps to monitor microservices via tools like AWS CloudWatch or Azure Monitor.

Now, let’s understand how database management play a critical role in maintaining the integrity of cloud computing for enterprise-grade applications. 

12.Database Management

Database management involves designing, deploying, querying, and optimizing structured and unstructured datasets in the cloud. It’s a foundational prerequisite for cloud computing because data powers almost every cloud application for transactions, analytics, personalization, or logging. 

Cloud-native databases offer features like auto-scaling, high availability, and managed backups, allowing you to focus on schema design, query efficiency, and integration rather than server maintenance.

• Relational Databases (RDBMS): Relational DBMS use fixed schemas with tables, rows, and columns. SQL is the primary query language. Services like Amazon RDS, Azure SQL Database, and Google Cloud SQL manage tasks such as replication, backups, and failover without manual configuration.
• NoSQL Databases: Ideal for unstructured or rapidly changing data, NoSQL databases like MongoDB, DynamoDB, or Couchbase allow flexible schemas. These are often used for real-time apps, IoT, or systems that ingest unpredictable data formats like JSON or logs.
• Data Warehousing: Data warehousing is optimized for complex analytical queries and reporting. Platforms like Google BigQuery, Amazon Redshift, and Azure Synapse handle petabyte-scale workloads and support SQL-like queries across large datasets.
• Query Optimization: Indexing strategies, query planners, normalization vs denormalization, and use of materialized views help reduce latency in high-read or transactional environments.
• Replication and Sharding: Multi-zone or multi-region replication ensures fault tolerance, while sharding allows horizontal data scaling. Services like Azure Cosmos DB and Amazon DynamoDB Global Tables handle this automatically.
• Backup, Restore, and High Availability: Built-in snapshotting, PITR (Point-in-Time Recovery), and cross-region backups are essential in production-grade database systems. Cloud platforms often provide automated retention and alerting for failed operations.

Example Scenario:

An e-commerce company in Pune uses Azure SQL Database to manage its product inventory and order tracking. Logging user activity across devices uses MongoDB Atlas hosted on AWS. Weekly sales reports are run on BigQuery.  You monitor query latency and optimize indexes to maintain —sub-second performance during seasonal traffic peaks.

Now, let’s understand does cloud computing needs coding for organizational applications. 

Does Cloud Computing Require Coding?

Whether cloud computing requires coding depends on your role and responsibilities. Technical roles like cloud developers and DevOps engineers need strong programming skills to build, automate, and scale cloud-native applications. 

In contrast, infrastructure-focused or governance roles may emphasize configuration, policy enforcement, or platform administration over deep coding. However, even basic scripting knowledge enhances efficiency in almost every cloud function by enabling automation and customization.

Role-Based Coding Requirements

• Cloud Architect: Requires scripting and template authoring (e.g., Terraform, CloudFormation) for infrastructure design and automation.
• Cloud Engineer: Strong coding is essential to build deployment scripts, troubleshoot services, and integrate APIs.
• Cloud Developer: Heavy coding role, builds applications, serverless functions, and microservices using languages like Python, Java, or Node.js.
• DevOps Engineer: Requires automation scripting (Bash, Python) and pipeline setup (Jenkins, GitLab CI/CD) for continuous delivery.
• Cloud Security Analyst: Focuses more on IAM policies, auditing, and tool-based controls, but basic scripting helps with log analysis and automation.
• Cloud Administrator: Involves managing cloud services and monitoring usage; coding optional but scripting improves operational tasks.
• Site Reliability Engineer (SRE): Uses programming for infrastructure automation, observability, and building self-healing systems.

Use Case:

A logistics startup in Bengaluru manages infrastructure across AWS and Azure using Terraform to automate multi-cloud provisioning. Python scripts handle alerting, log processing, and credential rotation, reducing manual effort. This coding-driven workflow enables faster deployments, consistent environments, and efficient scaling across regions in real-time cloud computing operations.

Now, let’s explore what are the common tools and technologies for cloud computing. 

What Are the Tools and Technologies Required in Cloud Computing?

Cloud computing tools span across development, automation, security, monitoring, and orchestration. Coding tools like IDEs, Git, and CI/CD platforms are critical for writing and deploying cloud-native applications, while non-coding tools support infrastructure and disaster recovery. 

Containerization (e.g., Docker, Kubernetes) and Infrastructure as Code (e.g., Terraform, CloudFormation) are central to building scalable and repeatable environments. Mastery of these technologies ensures reliable deployment, efficient resource management, and security compliance in multi-cloud operations.

Here’s a breakdown of coding and non-coding tools, along with examples of popular platforms used in cloud environments.

1. Coding Tools

Coding tools are essential for tasks like application development, automation, and managing infrastructure. 

These tools – tabulated below – allow cloud professionals to write, debug, and optimize code for cloud environments.

2. Non-Coding Tools

Non-coding tools – tabulated below – focus on managing, monitoring, and securing cloud environments. They are ideal for roles like cloud administration, security, and operations.

3. Popular Cloud Platforms

Here are some platforms that provide a comprehensive suite of tools for developing, deploying, and managing cloud applications.

• Amazon Web Services (AWS): Known for its wide range of services, including computing, storage, and AI tools.
• Microsoft Azure: Offers seamless integration with Microsoft products and enterprise-grade solutions.
• Google Cloud Platform (GCP): Excels in data analytics, machine learning, and scalable storage.

Here are some platforms that provide a comprehensive suite of tools for developing, deploying, and managing cloud applications.

• Amazon Web Services (AWS): Known for its wide range of services, including computing, storage, and AI tools.
• Microsoft Azure: Offers seamless integration with Microsoft products and enterprise-grade solutions.

Google Cloud Platform (GCP): Excels in data analytics, machine learning, and scalable storage.

Use Case

A healthcare analytics company in Hyderabad uses Visual Studio Code and GitLab CI/CD to develop Python-based microservices. Your DevOps team deploys the services using Docker containers orchestrated through Kubernetes, while AWS CloudWatch monitor system performance and threats across cloud workloads. This integrated toolchain enables secure, automated deployment pipelines across both AWS and Azure with minimal downtime.

Also read: The Future of Cloud Computing: Future Trends and Scope 2025

Enhance your expertise with our Software Development Free Courses. Explore the programs below to find your perfect fit.

Explore Our Software Development Free Courses

How Can upGrad Help You Build a Career?

Cloud computing does require coding, along with a command of networking, virtualization, operating systems, and database systems, these are the top prerequisites for cloud computing. To build and manage scalable cloud-native architectures, you must combine infrastructure automation skills with hands-on experience in CI/CD, and containerization. 

Focus on mastering these areas through real-world projects, cloud certification labs, and scripting tasks to develop the applied expertise needed in production environments.

If you want to learn industry-relevant cloud computing skills, look at upGrad’s courses that allow you to be future-ready. These are some of the additional courses that can help understand cloud computing​.

• Master’s Degree in Artificial Intelligence and Data Science
• Generative AI Mastery Certificate for Software Development

Curious which courses can help you gain expertise in cloud computing? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center.