Citation:

Santosh Kumar Singu, 2022. "Performance Tuning Techniques for Large-Scale Financial Data Warehouses", ESP Journal of Engineering & Technology Advancements, 2(4): 116-129.

Abstract:

The Bigger and denser the financial data gets, the more imperative it becomes to have good DW systems to store and analyze data from it. Another data integration issue, which has been recognized as crucial to financial institutions especially, is the ability to formulate large-scale data warehouses for intelligent decision support, reporting, and forecasting. Often, performance tuning of large-scale financial data warehouses requires adjustments to all these software levels and the systems’ hardware and database designs with reference to high availability, scalability, and velocity. In this paper, various performance-tuning strategies that enhance data acquisition, query response, and resource utilization for financial data warehouses are presented. We look at methods such as indexing, partitioning, materialized view, parallel processing, query optimization, and more advanced processes such as in-memory processing and data compression. The paper also identifies workload management and the utilization of local and cloud-based resources in financial data warehouses. Specific concerns that need to be taken into account when performance tuning a financial data warehouse comprise real time data analysis, compliance issues and risk management constraints of the financial business. The most important characteristic of financial data is that the values can be measured on different levels of volume, velocity and variety – and this reality responds to the need for scalability when building effective data warehouses. By leveraging distributed database systems, cloud services, and the most recent developments in big data technologies, financial institutions can control data better and at a lower cost. The paper also presents various case studies and recent research articles which prove the feasibility of the above techniques in huge financial contexts. We also talk about the issues and drawbacks related to data privacy issues and breaches, as well as the pros and cons of choosing a particular tuning technique. The conclusion gives future directions in terms of the development of new trends like AI-integrated query optimization direction and the effect of blockchain on the performance of financial data warehousing systems.

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[21] Santosh Kumar Singu, 2021. "Designing Scalable Data Engineering Pipelines Using Azure and Databricks", ESP Journal of Engineering & Technology Advancements, 1(2): 176-187.

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Keywords:

Data Warehousing, Financial Data, Performance Tuning, Query Optimization, Indexing, Partitioning, In-Memory Processing, Big Data.