Develop a simplified distributed MapReduce framework that processes large datasets across multiple worker nodes by dividing tasks into map and reduce phases, improving computational efficiency and scalability.
Design master-worker coordination model.
Implement data partitioning logic.
Develop map function execution.
Implement reduce aggregation phase.
Handle worker node failures.
Add intermediate data storage mechanism.
Measure processing time.
Deploy across multiple machines.
Simulate big data processing.
Optimize task distribution.
Implement logging system.
Compare performance with single-node execution.
Document findings and analysis.
