Project Idea : Log Processing and Alerting System

P.S. this content below is generated by GPT.

Scenario: A company wants to monitor its server logs and raise alerts if certain patterns or errors appear. This project aims to build a system that ingests logs, processes them, and triggers alerts based on specific conditions.

Architecture:

Simulated Log Generation:
    Create a script that generates log files with various severity levels (INFO, WARN, ERROR).
    Send logs as messages to SQS using LocalStack.

Log Processing (Lambda):
    A Lambda function reads messages from the SQS queue.
    The function checks for certain patterns (e.g., ERROR logs) and, if a critical issue is found, sends an alert message to another SQS queue or SNS (both simulated with LocalStack).

Alert Handling (S3 or DynamoDB):
    Store alert messages in DynamoDB for logging and audit purposes.
    Alternatively, write alerts to an S3 bucket where they can be processed later for a summary report.

Email Notification Simulation:
    Simulate Amazon SNS to send email notifications when critical errors are detected.
    Use a Python script or a simple HTTP server to simulate the email sending service triggered by SNS.

Report Generation:
    Write a Python script that reads from DynamoDB or S3 and generates a daily report of logs and alerts.
    Visualize the report data using charts to show the frequency of different error types.

What You’ll Learn:

How to handle real-time log data using SQS.
Using Lambda to filter and process logs.
Implementing alerting mechanisms with SNS.
Storing log data in DynamoDB for further analysis.
Building a simple monitoring and alerting system.