Real-Time Data Processing with AWS Lambda in Golang: A Hands-On User Story

 

User Story: Efficient Real-Time Data Processing with AWS Lambda in Golang

Background:

A financial technology startup aims to enhance its customer experience by providing real-time financial insights and personalized recommendations. To achieve this, they need a system that can process vast amounts of third-party user data rapidly and reliably.

Challenge:

The main challenge is to process and validate large datasets fetched from various financial data providers with low latency. The system must handle data inconsistencies, network failures, and scale according to demand without incurring high infrastructure costs.

Solution:

Deploy an AWS Lambda function written in Golang, which is known for its efficiency in concurrent processing and low-latency operations. The Lambda function will trigger on an event basis, processing data fetched from third-party APIs.

Technical Details:

• Lambda Function in Golang: The Lambda function is implemented in Golang to leverage Go’s powerful concurrency features and efficient memory management. The function fetches data from third-party APIs, processes it, and validates the integrity and format of the data.

• Error Handling and Retry Logic: Implement robust error handling within the Lambda function to manage and retry failed data fetches and validations, ensuring reliability even when third-party services are unstable.

• DynamoDB with DAX: Store the processed data in Amazon DynamoDB, utilizing DAX (DynamoDB Accelerator) for caching responses. This approach reduces data retrieval times and helps maintain the performance SLA of 250ms.

• Performance Metrics and SLA: The system aims to keep the round-trip time for processing and storing data under 250 milliseconds. Detailed logging and real-time monitoring are set up to track this SLA.

• Testing Strategy:

• Unit Testing: Write unit tests for individual components of the Lambda function using Go’s testing framework.
• Integration Testing: Test the integration of the Lambda function with DynamoDB and third-party APIs to ensure the system works as expected in a live environment.
• Load Testing: Simulate traffic to evaluate how the system performs under different loads, ensuring it can handle peak data volumes efficiently.
  
  
  
  

Acceptance Criteria:

1. Data Fetch and Processing:

   • The system retrieves data from third-party APIs successfully.
   • Data fetched matches the expected format and schema.
   • All data processing completes within the 250ms performance SLA.

2. Error Handling and Retry Logic:

   • The system retries data fetching up to three times in case of failures.
   • Errors and exceptions are logged with sufficient detail to diagnose issues.
   • If data cannot be fetched after retries, the system sends an alert to the designated monitoring dashboard.

3. Data Storage and Caching:

   • Processed data is stored in DynamoDB within 100ms of processing.
   • DynamoDB Accelerator (DAX) effectively caches data, reducing subsequent access times by at least 50%.
   • Data integrity checks confirm that no data corruption occurs during storage.

4. Testing:

   • Unit tests cover at least 90% of the Lambda function’s codebase.
   • Integration tests verify that the function interacts correctly with DynamoDB and third-party APIs.
   • Load tests confirm that the system handles at least 10,000 requests per minute without degradation of performance SLA.

5. Monitoring and Logging:

   • System performance and errors are monitored in real-time.
   • Logs include timestamps, error details, and identification of the affected components.
   • Alerts for critical issues reach the monitoring dashboard within 1 minute of occurrence.

6. Documentation and Usability:

   • Documentation is complete, detailing setup, deployment, and troubleshooting steps.
   • External developers can set up the Lambda function in a new environment within 30 minutes using the provided documentation.
     
     
     
     
     
     To Be continued...