Migrating and Stabilizing a Python Web-Scraping Pipeline on AWS Lambda

Client 

Data Services Company

Partner 

AumniTech 

Background 

Data Services Company operates a data-driven platform that relies on timely and accurate public-record information to support alumni engagement and insights. As part of this effort, Data Services Company built a Python-based web obituary scraping solution to extract structured data from publicly available sources. 

The original implementation was configured to run in Groq, but for scalability, cost efficiency, and tighter integration with their cloud ecosystem, Data Services Company decided to migrate the workload to AWS Lambda. 

While the core migration was mostly complete, a few critical issues prevented the system from functioning end-to-end in AWS. 

Challenge 

After porting the Python code to AWS Lambda: 

• The Lambda function successfully executed and read input files, but 
• No data was returned or persisted after execution 
• IAM permissions, Lambda configuration, and networking appeared correct 
• All source code and configurations were maintained in a GitHub repository 

Despite appearing minor, the issue required deep expertise in AWS Lambda execution models, Python runtime behavior, and cloud-native debugging to identify and resolve. 

AumniTech’s Approach 

AumniTech was engaged to quickly diagnose and stabilize the solution. 

1. Rapid Code & Architecture Review 

• Reviewed the full Python codebase in GitHub 
• Analyzed differences between Groq execution behavior and AWS Lambda’s stateless runtime 
• Identified assumptions in the code that did not translate cleanly to Lambda 

2. Lambda Runtime & Execution Debugging 

• Validated handler configuration and execution flow 
• Traced input ingestion vs. output generation paths 
• Identified issues related to: 
• Return values vs. side-effects (writes/logs) 
• Lambda timeouts and silent failures 
• Missing or mis-handled response objects 

3. IAM, Environment, and Dependency Validation 

• Confirmed IAM policies for data access were correct 
• Ensured Python dependencies were packaged properly for Lambda 
• Validated environment variables and execution context assumptions 

4. Targeted Fixes (Minimal Refactor) 

• Implemented precise fixes rather than a full rewrite 
• Ensured scraped data was properly returned, logged, or persisted (as intended) 
• Aligned the function’s output behavior with AWS Lambda best practices 

Outcome 

• ✅ Python scraper successfully executed in AWS Lambda 
• ✅ Input files processed and data correctly returned 
• ✅ No changes required to IAM or overall AWS architecture 
• ✅ Solution fully compatible with GitHub-based CI/CD workflows 
• ✅ Migration completed with minimal effort and downtime 

What initially appeared as a blocking issue was resolved quickly by applying cloud-native execution expertise rather than broad refactoring. 

Business Impact 

• Enabled Data Services Company to standardize on AWS for serverless workloads 
• Improved scalability and operational reliability 
• Reduced dependency on non-AWS runtimes 
• Positioned the solution for future enhancements (Step Functions, S3 pipelines, EventBridge, etc.)