Implementation of Database Massively Parallel Processing System to Build Scalability on Process Data Warehouse

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Abstract

The problem with telecommunications companies today is that transactional data is more extensive than existing source tables. This makes business reporting less efficient and overwhelms query processing results in data warehouses so that they do not meet business requirements. The fast and complex evolution of the digital world must be scalable to the data warehouse process, so that the authors implement it in the data warehouse using massive parallel processing (MPP) with the Greenplum database, so that business users can get reports faster and more optimally. This case study explains how the MPP system implements and measures the performance of the Greenplum database by performing complex queries in the data warehouse with parallel processing. Therefore, this case study analyzes whether the use of MPP systems can measure the scalability of throughput and the response time in the data warehouse so that system performance in the Greenplum database remains stable for daily, weekly, and monthly operations.

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APA

Daeng Bani, F. C., Suharjito, Diana, & Girsang, A. S. (2018). Implementation of Database Massively Parallel Processing System to Build Scalability on Process Data Warehouse. In Procedia Computer Science (Vol. 135, pp. 68–79). Elsevier B.V. https://doi.org/10.1016/j.procs.2018.08.151

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