Building Scalable Data Warehouses: Best Practices and Case Studies (original) (raw)

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.

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Architecture and Performance of Data Warehouses

FUNDACION LAZARO, 2023

A Data Warehouse is characterized by a huge amount of data centralized in a single database. A Data Warehouse architecture is a method of defining the overall architecture of data communication processing and presentation that exist for end-clients. Architecture is an important part of any IT infrastructure because it helps to optimize the performance of the entire system. Query processing in centralized Data Warehouse is different from query processing in distributed Data Warehouse due to the amount of data processed at each site. A three-tier architecture provides an efficient query processing as compared to two-tier architecture because of the presence of precomputed results present in the middle tier. In this research paper, we discuss the primary types of architectures available and the query performance metrics. We are simulating the performance of Data Warehouse system in two types of architectures The result of our simulation clearly shows how the query performance of distributed Data Warehouse and three tier architecture is efficient as compared to their respective counterparts.

A survey of parallel and distributed data Warehouses

International Journal of Data Warehousing and Mining, 2009

Data Warehouses are a crucial technology for current competitive organizations in the globalized world. Size, speed and distributed operation are major challenges concerning those systems. Many data warehouses have huge sizes and the requirement that queries be processed quickly and efficiently, so parallel solutions are deployed to render the necessary efficiency. Distributed operation, on the other hand, concerns global commercial and scientific organizations that need to share their data in a coherent distributed data warehouse. In this paper we review the major concepts, systems and research results behind parallel and distributed data warehouses.

Study of Data Warehouse Architecture.

International Journal of Engineering Sciences & Research Technology, 2013

Data warehousing is the essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing with an emphasis on their new requirements and alsodefine back end tools for extracting, cleaning and loading data into a data warehouse, front-end client tools for querying and data analysis and tools for metadata management and for managing the warehouse.

Design and management of data warehouses report on the DMDW'99 workshop

ACM SIGMOD Record, 1999

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An Alternative Data Warehouse Reference Architectural Configuration

Lecture Notes in Computer Science, 2009

In the last few years the amount of data stored on computer systems is growing at an accelerated rate. These data are frequently managed within data warehouses. However, the current data warehouse architectures based on n-ary-Relational DBMSs are overcoming their limits in order to efficiently manage such large amounts of data. Some DBMS are able to load huge amounts of data nevertheless; the response times become unacceptable for business users during information retrieval. In this paper we describe an alternative data warehouse reference architectural configuration (ADW) which addresses many issues that organisations are facing. The ADW approach considers a Binary-Relational DBMS as an underlying data repository. Therefore, a number of improvements have been achieved, such as data density increment, reduction of data sparsity, query response times dramatically decreased, and significant workload reduction with data loading, backup and restore tasks.

Towards the Development of Large-Scale Data Warehouse Application Frameworks

Lecture Notes in Business Information Processing, 2012

Facing with growing data volumes and deeper analysis requirements, current development of Business Intelligence (BI) and Data warehousing systems (DWHs) is a challenging and complicated task, which largely involves in ad-hoc integration and data re-engineering. This arises an increasing requirement for a scalable application framework which can be used for the implementation and administration of diverse BI applications in a straight forward and cost-efficient way. In this context, this paper presents a large-scale application framework for standardized BI applications, supporting the ability to define and construct data warehouse processes, new data analytics capabilities as well as to support the deployment requirements of multi scalable front-end applications. The core of the framework consists of defined metadata repositories with pre-built and function specific information templates as well as application definition. Moreover, the application framework is also based on workflow mechanisms for developing and running automatic data processing tasks. Hence, the framework is capable of offering an unified reference architecture to end users, which spans various aspects of development lifecycle and can be adapted or extended to better meet application-specific BI engineering process.

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