Hello Dev Community,
I’m excited to share that I’ve completed my AWS Certified Cloud Practitioner course! This milestone marks the end of one chapter and the beginning of a new adventure in my journey to becoming a Cloud Engineer. Over the past two weeks, I’ve studied various topics and services fundamental to AWS cloud operations.
Starting next week, I’ll be diving into hands-on projects across different skill levels:
- Level 100 (Introductory)
- Level 200 (Intermediate)
- Level 300 (Advanced)
- Level 400 (Expert)
These projects will provide practical experience and deepen my understanding of AWS services in real-world scenarios.
Key Topics Covered During My Learning
Provisioning & Provisioning Services
Provisioning Basics:
I learned the processes behind efficiently provisioning cloud resources, understanding their lifecycle and how to scale and manage them effectively.
Provisioning Services:
AWS offers services to simplify and automate provisioning, helping manage resources in a scalable, cost-effective manner with minimal manual intervention.
AWS Elastic Beanstalk
A major highlight was working with AWS Elastic Beanstalk, a fully managed Platform-as-a-Service (PaaS) that simplifies deploying and managing applications.
Key Features:
- Automatic Scaling: Automatically adjusts instances based on demand.
- Load Balancing: Distributes traffic across instances to maintain high availability.
- Monitoring: Integrates with AWS CloudWatch to monitor health and performance.
Introduction to Machine Learning (ML) & Artificial Intelligence (AI)
I explored the fundamentals of AI and ML, understanding how these technologies shape industries through predictive analytics, recommendation engines, and natural language processing (NLP).
AI & ML Services on AWS:
- Amazon SageMaker: Build, train, and deploy ML models with ease.
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AWS AI Services: Tools like:
- Amazon Rekognition for image/video analysis.
- Amazon Comprehend for NLP.
- Amazon Polly for text-to-speech.
Big Data & Analytics Services
With data growing exponentially, I studied AWS services for handling large datasets:
- Amazon Redshift: A fully managed data warehouse for querying structured data.
- Amazon EMR (Elastic MapReduce): Used for big data processing with frameworks like Apache Spark and Hadoop.
Amazon QuickSight
AWS’s business intelligence tool that creates interactive dashboards and visualizations. It was amazing to transform raw data into visual insights.
AWS Well-Architected Framework Overview
The AWS Well-Architected Framework is fundamental for designing scalable, secure, high-performing, and cost-effective systems. The five pillars of this framework are:
- Operational Excellence
- Security
- Reliability
- Performance Efficiency
- Cost Optimization
Detailed Learning Insights
Performance Efficiency
Performance optimization is about using the right resources for the job. I explored services like Auto Scaling and Elastic Load Balancing to maintain efficiency as workloads change.
Cost Optimization
I focused on strategies to reduce costs while maintaining performance and reliability. This included:
- Leveraging Reserved Instances.
- Optimizing storage.
- Right-sizing infrastructure based on usage.
AWS Well-Architected Tool
This tool helps assess existing workloads and provides recommendations for improvement.
Total Cost of Ownership (TCO) & CAPEX vs OPEX
Understanding TCO helps businesses assess the financial impact of cloud adoption. Additionally, I explored the shift from CAPEX (upfront physical infrastructure) to OPEX (ongoing operational costs) and how this model offers flexibility.
AWS Pricing Calculator
I practiced estimating costs for AWS services using this tool, which offers insights into cloud budgeting and cost optimization.
Migration Tools
I explored tools like:
- VM Import/Export for transferring virtual machines.
- AWS Database Migration Service (DMS) for migrating databases with minimal downtime.
Billing and Pricing
I explored AWS’s Free Tier and Free Services, as well as programs that offer AWS Credits to help reduce costs.
AWS Support & Marketplace
I studied the various AWS Support Plans and the AWS Marketplace for deploying third-party solutions.
Cost Management Tools
I explored tools like:
- AWS Budgets and Budget Reports to track costs.
- Cost Allocation Tags for efficient tracking of spending.
- Billing Alarms and Cost Explorer for monitoring expenditures.
Security Fundamentals
Defense-In-Depth & CIA Triad
Security was a major focus. I explored the Defense-In-Depth approach and the CIA Triad (Confidentiality, Integrity, Availability), which are essential for cloud security.
Encryption & Cryptography
I learned about in-transit and at-rest encryption, cryptographic keys, and techniques like hashing and salting to secure sensitive data.
Compliance & Security Tools
I examined AWS tools like:
- AWS Inspector for vulnerability scanning.
- AWS Shield for DDoS protection.
- Amazon Macie for detecting sensitive data like PII.
AWS Services: Comparative Insights
Load Balancers
I studied the four types of Elastic Load Balancers (ELBs):
- Classic Load Balancer (CLB)
- Application Load Balancer (ALB)
- Network Load Balancer (NLB)
- Gateway Load Balancer (GWLB)
Conclusion
Reflecting on my AWS Cloud Practitioner journey, I’ve gained a deep understanding of AWS tools and services. From foundational topics like security and cost management to advanced services in machine learning and data analytics, I’ve built a solid foundation in cloud computing.
This journey has not only sharpened my technical skills but also prepared me for the next steps in my cloud computing career. I’m excited to continue exploring, learning, and building with AWS!
Asif Khan — Aspiring Cloud Architect | Weekly Cloud Learning Chronicler
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