Optimizing Big Data Processing: An Analysis of Hadoop's Master-Slave Architecture and the Emergence of Serverless Solutions

This paper presents an in-depth analysis of Hadoop's master-slave architecture, a cornerstone of large-scale data processing frameworks. The architecture's key features include the separation of responsibilities between the master node (NameNode), which manages metadata and data placement, and multiple slave nodes (DataNodes), which handle data storage and processing. Hadoop's design facilitates fault tolerance through data replication, scalability by adding DataNodes, and high throughput via parallel processing across nodes. Additionally, features such as rack awareness, heartbeat mechanisms, and simplified data access contribute to its robustness in handling big data workloads. However, the architecture faces challenges such as single points of failure, scalability limits, and network bottlenecks, particularly in on-premise deployments. To address these issues, the paper explores the potential of serverless architectures as a modern alternative for big data processing. By examining implementations across leading cloud platforms—AWS, Azure, and Google Cloud—the study highlights how serverless models can offer improved scalability, operational efficiency, and cost-effectiveness, while simplifying infrastructure management. Although serverless solutions introduce their own limitations, such as cold start latencies and vendor lock-in, they provide a compelling option for organizations with variable or unpredictable workloads. Ultimately, the paper emphasizes that while Hadoop's master-slave architecture remains highly effective, the growing adoption of serverless technologies presents new opportunities to optimize big data processing in an increasingly cloud-driven world.