Cloud DevOps Essentials - Part 3

THE SERIES FINALE......

Author

Vishakha Sadhwani
May 29, 2025

In partnership with

Hi Inner Circle,

Here’s the final set of high-impact areas I think are worth your focus right now. With AI moving fast and cloud roles getting more hands-on, it’s not just about keeping up — it’s about picking the right problems to solve.

These topics aren’t just trends — they’ve come up consistently in interviews, where you're expected to go beyond just core topics and tools.

Let’s dive in.

Configuration & Patch Management

Core Concepts

  • Desired State Configuration (DSC): Defining and enforcing the ideal configuration for servers and infrastructure as code.

  • Immutable Infrastructure: Building new servers with desired configurations and replacing old ones, rather than updating existing ones (this comes often with terraform)

  • Automated Patching: Systematic and automated application of software updates and security patches.

  • Idempotency: Operations can be applied multiple times without changing the result beyond the initial application, crucial for safe automation (also a part of Infrastructure as code component)

Popular Tools

  • Ansible: Agentless, simple YAML syntax for automation.

  • Chef: Ruby-based - desired state management.

  • Puppet: Ruby-based, strong for large-scale configuration.

  • AWS Systems Manager: Integrated AWS service for instance management, patching, inventory.

  • Azure Automation: Microsoft's cloud-based automation and configuration management service.

  • Google Cloud OS Config: GCP service for VM management, patching, and inventory.

Architecture & Design for Cloud Native

Core Concepts

  • Microservices Architecture: Breaking down applications into small, independent services communicating via APIs.

  • Serverless/FaaS (Function as a Service): Executing code in response to events without managing underlying servers.

  • Containerization: Packaging applications and dependencies into portable, isolated units (containers).

  • Event-Driven Architecture (EDA): Services communicate asynchronously through events (e.g., message queues).

  • Service Mesh: A dedicated infrastructure layer for managing service-to-service communication.

  • API Gateways: A single entry point for clients to access multiple microservices, handling routing and security.

Popular Tools (You already know this via Part 1&2)

  • Docker: Containerization runtime and ecosystem.

  • Kubernetes: Container orchestration platform.

  • Istio / Linkerd: Service Mesh implementations.

  • AWS API Gateway / Azure API Management / Google Cloud Apigee: Cloud-native API Gateway services.

  • Apache Kafka / Amazon Kinesis / Google Cloud Pub/Sub: Distributed streaming platforms for EDA.

  • AWS Lambda / Azure Functions / Google Cloud Functions: Serverless/FaaS platforms.

Data & Database DevOps (high-level understanding is okay!)

Core Concepts

  • Database Version Control: Storing database schema changes and scripts in a version control system (e.g., Git).

  • Automated Database Migrations: Automating the application of schema changes as part of the CI/CD pipeline

  • Data Masking/Anonymization: Creating realistic, non-sensitive data for non-production environments.

  • Database as Code: Treating database schemas and configurations as declarative scripts managed in code.

  • Data Pipeline Automation: Automating the entire lifecycle of data ingestion, transformation, and loading (ETL/ELT).

Popular Tools (Just Skim this)

  • Liquibase: Database schema change management.

  • Flyway: Database migration tool.

  • Redgate SQL Change Automation: For SQL Server database CI/CD.

  • dbt (data build tool): For data transformation and modeling in data warehouses.

  • Apache Airflow / Prefect / Dagster: Data workflow orchestration.

  • Git: For version control of database scripts.

DevSecOps (DevOps + Security)

Core Concepts

  • Shift-Left Security: Integrating security practices early and continuously throughout the SDLC.

  • Security as Code: Defining security policies and configurations in machine-readable code for automation.

  • Automated Security Testing: Incorporating SAST, DAST, SCA, and IaC scanning into CI/CD pipelines.

  • Secrets Management: Securely storing, distributing, and rotating sensitive credentials and API keys.

  • Runtime Security & Cloud Security Posture Management (CSPM): Monitoring and enforcing security policies for deployed applications and cloud infrastructure in real-time.

Popular Tools (Good to know)

  • SAST: SonarQube.

  • DAST: OWASP ZAP, Burp Suite.

  • SCA: Snyk, Dependabot.

  • IaC Security Scanners: Checkov, Trivy.

  • Secrets Management: HashiCorp Vault, AWS Secrets Manager, Azure Key Vault.

  • CSPM: Wiz, Orca Security, Prisma Cloud.

  • WAF (Web Application Firewalls): AWS WAF, Cloudflare WAF.

14. MLOps (Machine Learning Operations)

Core Concepts

  • ML Experiment Tracking & Management: Logging and organizing ML experiment metadata for reproducibility.

  • Data Versioning & Management for ML: Tracking changes to datasets for training reproducibility.

  • Automated ML Pipelines (CI/CD for ML): Orchestrating the full ML workflow (data, train, eval, deploy) as an automated process.

  • Model Monitoring & Drift Detection: Continuously tracking model performance, data drift, and concept drift in production.

  • Feature Stores: Centralized repository for managing and serving ML features consistently.

  • Model Retraining & Lifecycle Management: Automating retraining models with new data and managing model versions.

Popular Tools (VERY IMPORTANT)

  • MLflow: Open-source platform for ML lifecycle management.

  • Kubeflow: Open-source platform for deploying and managing ML workflows on Kubernetes.

  • DVC (Data Version Control): Open-source system for data versioning.

  • TensorFlow Extended (TFX): Google's open-source platform for ML production pipelines.

  • Vertex AI (Google Cloud): Managed ML platform covering the entire ML lifecycle.

  • AWS SageMaker: Comprehensive ML service suite from AWS.

  • Feast: Open-source Feature Store.

You already know about the free resources now:

What's Next after these Advanced Topics?

For these advanced areas, the goal isn't just clicking around; it's about architecting, integrating, and optimizing complex systems.

Let’s do this next:

  1. Pick a Project: Not just a service

  2. Deep Dive into a Niche: Select one or two specific tools or concepts within a group and try to gain expertise.

  3. Focus on Real-World Challenges: Think about common problems in production environments (e.g., managing secrets at scale, ensuring data consistency in distributed systems, handling model drift) and design solutions.

  4. Leverage Cloud-Native Services: Learn to integrate and understand how they function

  5. Document Everything: For your resume, a well-documented project with architecture diagrams, code, and a README is invaluable.

  6. Showcase Your Work: Each project is a portfolio piece (Go publish it via blog/github/linkedin)

Check out the list of projects here — if you haven’t already. Good luck!

See you next week for another dose of cloud.

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