Week 8 Worklog

Week 8 Objectives:

  • Milestone: Successfully clear the Mid-term Exam.
  • Architectural Pivot: Transition from traditional EC2 hosting to a Serverless architecture (AWS Lambda, API Gateway) to reduce operational overhead.
  • AI Service Design: Architect an asynchronous image processing pipeline using Amazon Rekognition.
  • Database Modeling: Design the DynamoDB schema (Single Table Design vs. Multi-table) to support high-performance access patterns.

Tasks to be Deployed This Week:

DayTaskStart DateCompletion DateResources
Monday- Milestone- Final review and completion of the Mid-term Competency Exam.
- Post-exam sprint planning with the backend team.		10/28/202510/28/2025
Tuesday- Infrastructure Setup- Initialized the local development environment (AWS CLI, Boto3, IDE Linting).
- Designed the DynamoDB Articles table with Partition Key (PK) and Sort Key (SK) strategies.		10/29/202510/30/2025
Wednesday- Exam Day: Mid-term Assessment.
- API Exposure		- Configured API Gateway (REST API) to route HTTP requests to Lambda functions.
- Tested endpoints using Postman to verify payload structures.		10/31/202510/31/2025
Thursday- FaaS Development- Developed the first Lambda Functions in Python for core CRUD operations (CreatePost, GetPost).
- Implemented Boto3 wrappers to interact with DynamoDB SDK.		01/11/202501/11/2025
Friday- AI Pipeline Design- Designed the “Upload-Trigger” flow: User uploads image to S3 $\rightarrow$ S3 Event Notification $\rightarrow$ Lambda $\rightarrow$ AWS Rekognition.
- This decouples the upload latency from the processing time.		11/02/202511/02/2025

Week 8 Achievements:

  • Completed the mid-term exam (October 31st).
  • Successfully shifted the architectural paradigm to Serverless, eliminating the need for OS patching and server management.
  • Established the foundational API pipeline: Client $\rightarrow$ API Gateway $\rightarrow$ Lambda $\rightarrow$ DynamoDB.
  • Defined a scalable strategy for AI integration using event triggers rather than synchronous API calls.
  • Architected a scalable, asynchronous AI Processing Pipeline, decoupling user uploads from heavy image processing tasks (Event-driven).