Cloud Computing in Software Engineering

There was a time when we had to depend on pen drives, hoping that our laptops wouldn't crash, and believing that only the richest companies could afford scalable infrastructure. Now in 2025, and all the things you are going to create - applications, automations, AI systems, and analytics are based on cloud skills. The reality is nothing more than: 👉 If you are familiar with cloud technology, you could virtually create anything. 👉 If not, you will ultimately come to a limit in your career. Thus, I have condensed the whole cloud universe into a single road map easy to comprehend, organized, and suitable for beginners, so you can pinpoint what step to take next in your learning journey. 🔹 Here’s what the roadmap covers (explained in simple words): 1. Cloud Basics Understand what cloud computing really is, how services are delivered (IaaS/PaaS/SaaS), what public & private clouds mean, and how major providers like AWS, Azure, and GCP differ. 2. Compute & Networking Learn how virtual machines, containers, firewalls, networks, DNS, CDNs, autoscaling, and load balancers help your applications stay fast, secure, and available worldwide. 3. Storage & Databases Figure out how cloud platforms store your data - from object storage to block storage, SQL vs NoSQL databases, backup systems, and high-availability replicas. 4. Identity, Access & Security Master the essentials of IAM, permissions, secrets, encryption, and secure authentication - the backbone of cloud safety and compliance. 5. Serverless & Event-Driven Computing Explore how cloud functions, event buses, triggers, and stateless designs let you build systems that scale automatically without managing servers. 6. Infrastructure as Code (IaC) & Automation Learn Terraform, CloudFormation, Pulumi, and Ansible - the tools that help you deploy infrastructure in minutes, not days. 7. Monitoring, Logging & Observability Understand how CloudWatch, Azure Monitor, Prometheus, Grafana, and ELK Stack keep systems healthy, optimized, and predictable. 8. Security, Governance & Cost Optimization Discover how organizations secure their cloud assets, enforce policies, prevent misuse, and save thousands in cloud spending. Cloud is not just another skill, it’s the foundation of modern engineering. Whether you’re aiming for DevOps, AI engineering, backend development, data engineering, or security roles… 👉 Cloud knowledge multiplies your career opportunities. 👉 And the earlier you start, the faster you grow. For tech professionals & global talent seeking international opportunities 🌏 📌 Job search strategy + talent visa pathway insights 👉 Free insights session — https://lnkd.in/gXRFqxNu Follow Gaurav Mehta for more tech insights and updates.

𝐌𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐭𝐡𝐢𝐧𝐤 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚 𝐂𝐥𝐨𝐮𝐝 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐢𝐬 𝐚𝐛𝐨𝐮𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐨𝐧𝐞 𝐜𝐥𝐨𝐮𝐝. That belief quietly breaks careers. The real skill?  Knowing the full cloud stack and how the pieces fit together. 𝐓𝐡𝐢𝐬 𝐫𝐨𝐚𝐝𝐦𝐚𝐩 𝐬𝐡𝐨𝐰𝐬 𝐰𝐡𝐚𝐭 𝐦𝐨𝐝𝐞𝐫𝐧 𝐂𝐥𝐨𝐮𝐝 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐧𝐞𝐞𝐝 𝐢𝐧 𝟐𝟎𝟐𝟔: 𝟏. 𝐂𝐨𝐫𝐞 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 Everything starts with understanding how cloud services are delivered. - IaaS for infrastructure control - PaaS for faster application development - SaaS for ready-to-use platforms 𝟐. 𝐂𝐨𝐦𝐩𝐮𝐭𝐞 𝐚𝐧𝐝 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 This is where workloads actually run and data lives. - Virtual machines, containers, Kubernetes, and serverless - Object, block, and file storage - SQL, NoSQL, and data warehouse systems 𝟑. 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐚𝐧𝐝 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲 Cloud doesn't work without strong networking foundations. - Virtual networks, VPN, Direct Connect, ExpressRoute - CDN, global accelerators, API gateways, service mesh 𝟒. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 Most cloud failures start with weak security basics. - IAM and encryption in transit and at rest - Compliance requirements like GDPR, HIPAA, and SOC 2 𝟓. 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐃𝐞𝐬𝐢𝐠𝐧 This separates operators from real engineers. - High availability and disaster recovery - Microservices and event-driven architectures - Well-Architected Framework thinking 𝟔. 𝐃𝐞𝐯𝐎𝐩𝐬 𝐚𝐧𝐝 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 Manual cloud doesn't scale. - Infrastructure as Code using Terraform, Bicep, CDK, CloudFormation - CI/CD with Git, Jenkins, GitLab CI, and MLOps 𝟕. 𝐂𝐥𝐨𝐮𝐝 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 If you can't see it, you can't run it. - Logging, monitoring, and tracing - Predictive analytics and auto-remediation 𝟖. 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 Cloud engineers increasingly work with data systems. - Data warehousing like Redshift and BigQuery - ETL tools such as Glue and Dataflow - Real-time data using Kafka and Pub/Sub - Lakehouse architectures 𝟗. 𝐀𝐈 𝐚𝐧𝐝 𝐌𝐋 𝐢𝐧 𝐂𝐥𝐨𝐮𝐝 AI workloads are becoming default, not optional. - Managed AI services and ML platforms - MLOps tools like SageMaker and Vertex AI - Infrastructure for ML and container-based platforms 𝟏𝟎. 𝐂𝐨𝐬𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Good engineers design for cost from day one. - On-demand, reserved, and spot pricing models - Right-sizing, budgeting, and auto-scaling 𝟏𝟏. 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 This is where senior engineers stand out. - Cloud adoption frameworks - Tagging and policy-driven control - Multi-cloud and hybrid cloud strategies Which area do you think most Cloud Engineers ignore until it becomes a problem? ♻️ Repost this to help your network get started ➕ Follow Jaswindder for more #CloudEngineering #CloudRoadmap #DevOps 

Software Engineers just need to write code, right? Wrong. To me, the real test of an excellent software engineer is whether their code is “infrastructure aware.” Software development teams are often responsible for three product phases: development, deployment, and operation. The whole cycle's been wasted if the business logic they’ve baked into the code doesn’t correctly scale and run on the target production infrastructure. Of course, large organizations have supporting teams of DevOps and SREs to help with operations and troubleshooting. But if the code is terrible, no supporting team can resolve the problem proactively and systemically. The good news is that some of the issues developers struggle with can be mitigated. Kubernetes allows developers to test the scalability and resiliency of their applications because automation and orchestration are built in, especially with Amazon Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), Google Kubernetes Engine (GKE) or OpenShift. When applications require more robust network, storage, or security features, Infrastructure as Code and API-based tools are useful, but not a complete answer. Developers also gather feedback via observability and monitoring tools to plan for application improvements, enhancements, and modifications during the next development cycle. Again, when extended functionality beyond the basics is required, these efforts can become exponential. For each cloud, optimizations may need to be done differently; enhancements can behave differently on different clouds, and modifying and maintaining specific fixes on a per-cloud basis can lead to technical debt and a spaghetti bowl of different codebases depending on the target cloud. Infrastructure, data services, Kubernetes, and tools can be combined within Internal Development Platforms. With IDPs, dev teams can maintain operational excellence and simplicity despite the many edge cases and complexities they face. With this said, I forecast that IDPs and Platform Engineering remain growing trends in 2024. Happy Holidays!