Designing of a Decentralized Cluster Data Warehouse using Amdahl's and Gustafson's Law

Decentralized cluster data warehouses are popular owing to their scalability and availability. Performance and resource consumption must be considered while creating such systems. Amdahl's law argues that the proportion of the workload that cannot be parallelized limits a system's speedup. This law affects system design in decentralized cluster data warehousing. In creating a decentralized cluster data warehouse, Amdahl's law requires detecting and evaluating bottlenecks in data input, storage, processing, and query execution. Designers may improve parallelizable components by understanding non-parallelizable components. A decentralized cluster data warehouse needs data propagation. Keeping frequently accessible data close may impair system performance. This requires exact data partition, replication, and cluster node data placement. Decentralized cluster data warehouses need technical, organizational, and governance concerns. Data security, privacy, regulatory compliance, and accessibility are examples. John Gustafson and Edwin Barsis' Gustafson's Law stresses parallel computing's scalability and workload rise. Gustafson's Law allows scaling up the issue and using more computer resources to handle higher workloads, unlike Amdahl's Law. The law shows that as the issue size increases, the percentage of the program that may be processed in parallel becomes a bigger fraction of the entire execution time, improving efficiency and speedup. Gustafson's Law promotes reducing execution time by dividing task over numerous processors. This abstract compares Gustafson's Law with Amdahl's Law, revealing parallel computing's advantages for bigger problem instances and the impact of increasing workload in parallel efficiency.