Introduction: The Big Data problem, characterized by the three Vs. of Big Data - Verity, Volume, and Velocity, presented a significant challenge for traditional relational database management systems (RDBMS). To address this challenge, the industry sought new approaches and platforms capable of handling the complexities of Big Data. Two primary categories emerged: the monolithic approach, exemplified by systems like Teradata and Exadata, and the distributed approach, which utilizes clusters of interconnected computers. This article explores these approaches, comparing their scalability, fault tolerance, high availability, and cost-effectiveness to identify the evolution of Big Data processing.
The Monolithic Approach: The monolithic approach involves designing a large, robust system to handle all Big Data requirements. While systems like Teradata and Exadata predominantly support structured data, they are not categorized as Big Data systems but are developed using a monolithic approach. These systems rely on a single massive system with extensive CPU capacity, RAM, and disk storage. However, their limitations become apparent as data volumes and concurrent users increase.
The Distributed Approach: In contrast, the distributed approach employs multiple smaller systems working together to address larger problems. A distributed system functions as a cluster of interconnected computers with combined capacities equal to or exceeding that of a monolithic system. This approach offers superior scalability, fault tolerance, high availability, and cost-effectiveness.
Scalability: Scalability refers to a system's ability to increase or decrease performance based on demand. Monolithic systems have fixed capacities and require coordination with vendors to scale vertically, i.e., by adding resources to a single system. In contrast, distributed systems scale horizontally by adding more computers to the network. Horizontal scalability is simpler and faster, making the distributed approach more favorable.
Fault Tolerance and High Availability: Monolithic systems are prone to failure if a hardware component malfunctions, resulting in system downtime and reduced availability. On the other hand, distributed systems can tolerate multiple failures without compromising the overall system's functionality. The failure of a single computer within the cluster has minimal impact, ensuring higher availability.
Cost-Effectiveness: The distributed architecture offers cost advantages, allowing businesses to start with a small cluster and expand as needed. This approach accommodates average-quality machines, cloud environments, and rental options, making it more economically viable. With their complex scaling process and higher resource requirements, monolithic systems prove less cost-effective.
Conclusion: The industry recognized the need for a new approach or platform to tackle the Big Data problem. Engineers evaluated monolithic and distributed method and found that distributed systems excel in scalability, fault tolerance, high availability, and cost-effectiveness. Consequently, the development of Hadoop, a revolutionary Big Data processing platform, gained substantial attention and widespread adoption.
As technology continues to evolve, the challenges of Big Data processing persist. Organizations must carefully evaluate their data requirements and choose the appropriate approach or platform to effectively handle the vast amounts of data generated in today's data-driven world. The shift from monolithic systems to distributed systems marks an important milestone in the evolution of Big Data processing, enabling businesses to harness the power of data more efficiently and effectively.
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