Abstract:
Organisations must make the best decision at the appropriate time to obtain a
competitive advantage in a fast-changing market. To accomplish so, it's critical to
make faster and more efficient judgments based on near-real-time data analysis.
When it comes to these real-time streaming data analysis systems, the performance
of the database is having a huge impact on such applications as it is required to
achieve data availability and continuous processing for a large volume of data
without a delay. When it comes to streaming data, data warehousing is more
challenging. So, it is required to consider performance improvements in all the steps
of the Extraction, Transformation, Load (ETL) process and the database architecture
level. Therefore, the proposed approach is to improve the performance of the system
by optimising the ETL process (Extraction, Transformation, and Load) and real-time
data warehousing. In this approach, the optimised aggregation algorithm is
introduced. Apart from that, the hardware, storage schemas, and query optimization
of the data warehouse are also considered and this study is evaluating the
performance of the centralised architecture for the real-time data warehouse.
Citation:
Samaranayake, T.D.M.P. (2022). Improving the performance of real-time data analytics applications by optimising the database aggregations [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/22370