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Research Paper | Data & Knowledge Engineering | United States of America | Volume 12 Issue 12, December 2023 | Rating: 5.6 / 10
Comparing Different Strategies for Handling Type 2 Slowing Changing Dimensions in Data Warehousing
Abstract: In the ever-evolving data warehousing landscape, managing Type 2 Slowly Changing Dimensions (SCD) effectively maintains data accuracy and historical integrity. This article explores strategies for handling Type 2 SCD, comparing traditional and advanced approaches. Beginning by elucidating Type 2 SCD and its significance, the discussion delves into conventional methodologies such as full historical tracking, record overwriting, and timestamp-based solutions. Building upon this foundation, the article explores cutting-edge strategies, including temporal database design, hybrid approaches, and the integration of machine learning for dynamic SCD management. In addition to strategy comparison, the article offers practical insights into best practices for handling Type 2 SCD, covering data modeling, performance optimization, and monitoring and maintenance aspects. Real-world case studies highlight successful implementations, sharing valuable lessons from organizations navigating the challenges of managing evolving data dimensions. The article concludes by exploring emerging trends and technologies, shedding light on the future of data warehousing and the potential integration of blockchain and other innovations. This comprehensive analysis is a resource for data professionals seeking to optimize their data warehousing practices. It offers a roadmap for selecting the most suitable Type 2 SCD strategy based on specific use cases and evolving industry trends.
Keywords: Data Warehousing, Machine Learning in Data Management, Slowly Changing Dimensions, Temporal Database Design, Type 2 SCD Strategies
Edition: Volume 12 Issue 12, December 2023,
Pages: 1687 - 1695