Lambda
Intermediate2+ years experienceCloud & DevOps
Solid understanding with practical experience in multiple projects
My Experience
AWS serverless compute service for event-driven processing. Experienced in building scalable, cost-effective data processing pipelines.
Technical Deep Dive
Core Concepts I'm Proficient In:
• Serverless Architecture: Event-driven function execution without server management for scheduled web crawling operations
• Scheduled Execution: Configuring CloudWatch Events to trigger Lambda functions on weekly schedules for automated data collection
• Scalability: Leveraging auto-scaling compute for variable workloads without manual capacity planning
• S3 Integration: Triggering functions from S3 events and writing outputs back to S3 buckets for seamless data flow
• Cost Optimization: Pay-per-use model that eliminates idle resource costs for infrequent, scheduled operations
• Python Runtime: Deploying Python-based Lambda functions for web scraping and data processing workflows
Advanced Lambda Implementation Patterns:
• Scheduled Web Crawlers: Deploying weekly-triggered Lambda functions that execute web scraping operations and store results in S3
• Stateless Design: Building Lambda functions that operate independently without maintaining server-side state
• Event-Driven Processing: Configuring Lambda to respond to S3 upload events for automated downstream processing
• Execution Time Management: Designing functions to complete within Lambda timeout constraints for reliable operation
• Dependency Management: Packaging Python dependencies and web scraping libraries for Lambda deployment
• Logging and Monitoring: Implementing CloudWatch logging for debugging and performance tracking
Complex Problem-Solving Examples:
Scheduled Web Crawler Architecture:
Deployed Lambda functions for the AI Data Breach Hub that execute web crawling operations on a weekly schedule using CloudWatch Events triggers. These functions run Python-based scrapers that collect breach intelligence from various sources (PDFs, advisories, news sites), normalize the data to remove PII, and store results directly in S3 buckets. The serverless architecture proved ideal for this use case - since crawlers run once weekly, Lambda's pay-per-use model eliminates costs from idle servers while providing reliable, scheduled execution. Each function completes its scraping operations and terminates cleanly, writing collected data to organized S3 prefixes for downstream processing by ElasticSearch and MongoDB.
Single-Purpose Execution Pattern:
Designed Lambda functions with focused, single-purpose responsibilities optimized for their specific weekly execution schedule. Rather than building complex, multi-stage functions that might timeout or fail partially, implemented a clean execution model where each Lambda invocation performs one clear task: fetch data from specific sources, normalize it, and write to S3. This approach simplified debugging and monitoring while ensuring consistent, reliable data collection. The functions require minimal configuration and operate independently without complex state management or cross-function dependencies.
Areas for Continued Growth:
• Container Integration: Learning Lambda + Docker integration for deploying complex dependencies and custom runtime environments
• Microservices Patterns: Exploring how to architect multiple Lambda functions into cohesive microservices architectures
• Performance Optimization: Mastering cold start reduction, memory optimization, and execution efficiency for large-scale deployments
• Advanced Triggers: Deepening expertise in API Gateway integration, Step Functions orchestration, and event-driven architectures
Projects Using Lambda
2+ years
Experience
1
Projects
Intermediate
Proficiency