What are the various data integration techniques employed in data warehousing? Provide examples and scenarios where each technique is suitable.
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What are the various data integration techniques employed in data warehousing? Provide examples and scenarios where each technique is suitable.
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Solved in 3 steps
- In the context of data warehousing, what are the typical challenges associated with data integration, and how can they be addressed?Explain the ETL process in the context of data warehousing. What are its key components and why is it essential for data integration?What are the key components of a data warehousing architecture, and how do they work together to support data analysis and reporting?
- What is ETL in the context of data warehousing, and why is it essential for data integration?What are the primary challenges in designing and implementing a data warehousing system? How can they be addressed?How does data normalization and denormalization affect data warehousing design, and when should each be applied?
- How can data quality issues be addressed in a data warehousing environment?Explore the challenges of data integration in a data warehousing environment, especially when dealing with disparate data sources. How are these challenges typically addressed?Explain the concept of data warehousing as a hub for data integration. How can it facilitate a unified view of data from various sources?
- Describe the ETL (Extract, Transform, Load) process in the context of data warehousing. What are the key steps involved, and why is it crucial for data integration?Discuss the concept of slowly changing dimensions (SCD) in data warehousing. What are the different types of SCD, and how are they implemented?Explain the process of ETL (Extract, Transform, Load) in the context of data warehousing. What are the key steps involved, and why is ETL crucial for data integration?