Building Scalable Data Warehouses: Best Practices and Case Studies

Authors

  • Saketh Reddy Cheruku Independent Researcher, Pulimamidi Estates Beside Sri Sai Prashanthi Highschool Bhongir Nalgonda Highway, Bhongir Yadadrinbhongir (Dist) Telangana 508116
  • Shalu Jain Reserach Scholar, Maharaja Agrasen Himalayan Garhwal University, Pauri Garhwal, Uttarakhand
  • Anshika Aggarwal Independent Researcher, Maharaja Agrasen Himalayan Garhwal University, Uttarakhand, India

DOI:

https://doi.org/10.36676/mdmp.v1.i2.15

Keywords:

Scalable data warehouses, data modeling, ETL processes, cloud-based solutions, Salesforce Analytics, AI-powered insights

Abstract

In today's data-driven world, the ability to manage, store, and analyze large volumes of data is crucial for business success. The demand for scalable data warehouses has risen dramatically as organizations seek to handle the explosion of data generated by modern applications and digital transactions. "Building Scalable Data Warehouses: Best Practices and Case Studies" explores the key strategies, methodologies, and technologies involved in designing and implementing scalable data warehouses that meet the demands of today and the future. The paper highlights the importance of architecture choices, data modeling techniques, and performance optimization in creating data warehouses that can grow with an organization’s needs. Additionally, it provides case studies that demonstrate the real-world application of these principles in various industries, showing how scalable data warehouses have enabled companies to maintain high performance, reduce costs, and enhance decision-making capabilities.

The paper begins by defining what constitutes a scalable data warehouse, emphasizing the importance of a flexible and adaptive architecture that can accommodate growing data volumes and changing business requirements. It explores different architectural approaches, including the benefits and challenges of traditional on-premises data warehouses versus cloud-based solutions. The paper also discusses the role of data modeling, including dimensional and normalized approaches, in ensuring that the data warehouse can handle increasing complexity without compromising performance.

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Published

31-08-2024

How to Cite

Saketh Reddy Cheruku, Shalu Jain, & Anshika Aggarwal. (2024). Building Scalable Data Warehouses: Best Practices and Case Studies. Modern Dynamics: Mathematical Progressions, 1(2), 116–130. https://doi.org/10.36676/mdmp.v1.i2.15

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