Pearson eText for Modern Database Management -- Instant Access (Pearson+)
13th Edition
ISBN: 9780137305940
Author: Jeffrey Hoffer, Ramesh Venkataraman
Publisher: PEARSON+
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Chapter 9, Problem 9.31RQ
Program Plan Intro
Five errors and inconsistencies that are commonly found in operational data.
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Chapter 9 Solutions
Pearson eText for Modern Database Management -- Instant Access (Pearson+)
Ch. 9 - Prob. 9.1RQCh. 9 - Prob. 9.2RQCh. 9 - Prob. 9.3RQCh. 9 - List the five major trends that necessitate data...Ch. 9 - Prob. 9.5RQCh. 9 - Prob. 9.6RQCh. 9 - Prob. 9.7RQCh. 9 - Prob. 9.8RQCh. 9 - Prob. 9.9RQCh. 9 - Prob. 9.10RQ
Ch. 9 - List four objectives of derived data.Ch. 9 - Prob. 9.12RQCh. 9 - Prob. 9.13RQCh. 9 - Explain the pros and cons of logical data marts.Ch. 9 - Prob. 9.15RQCh. 9 - What is a helper table, and why is it often used...Ch. 9 - Describe the characteristics of a surrogate key as...Ch. 9 - Prob. 9.18RQCh. 9 - What is the purpose of conformed dimensions for...Ch. 9 - Prob. 9.20RQCh. 9 - Prob. 9.21RQCh. 9 - Prob. 9.22RQCh. 9 - Prob. 9.23RQCh. 9 - Prob. 9.24RQCh. 9 - Prob. 9.25RQCh. 9 - Describe the current key trends in data...Ch. 9 - Discuss the benefits of offering data warehousing...Ch. 9 - Prob. 9.28RQCh. 9 - List six typical characteristics of reconciled...Ch. 9 - Prob. 9.30RQCh. 9 - Prob. 9.31RQCh. 9 - Prob. 9.32RQCh. 9 - Prob. 9.33RQCh. 9 - Describe some field-level and record-level data...Ch. 9 - Prob. 9.35PAECh. 9 - Prob. 9.36PAECh. 9 - Prob. 9.37PAECh. 9 - Prob. 9.38PAECh. 9 - You are to construct a star schema for Simplified...Ch. 9 - Simplified Automobile Insurance Company would like...Ch. 9 - Prob. 9.41PAECh. 9 - Prob. 9.42PAECh. 9 - Prob. 9.43PAECh. 9 - Prob. 9.45PAECh. 9 - Prob. 9.46PAECh. 9 - Prob. 9.47PAECh. 9 - Prob. 9.48PAECh. 9 - Prob. 9.49PAECh. 9 - Prob. 9.50PAECh. 9 - Prob. 9.51PAECh. 9 - Prob. 9.52PAECh. 9 - Prob. 9.53PAECh. 9 - Prob. 9.54PAE
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- Describe the methods for dealing with data risks.Explanation of how each technique may represent the performance is also required. What is an idle case, and why is it used in pipelining?arrow_forwardHow does data normalization differ from denormalization in the context of data warehousing?arrow_forwardExplain the concept of data redundancy and how it can be minimized in a well-designed data model.arrow_forward
- What Are Some of the Most Frequent Data Modeling Errors?arrow_forwardHow would you categorize data based on Who, What, Where, and When in order to aid in the identification of the source or causes of this problem? What questions would you ask? What variables did you use to create your stratification?arrow_forwardFive flaws and inconsistencies that are often observed in operational data should be included below.arrow_forward
- How does data normalization and denormalization affect data warehousing design, and when should each be applied?arrow_forwardPlease provide us an overview of what makes a data input field reliable. There may be times when a departure from these guidelines is necessary, but under what circumstances?arrow_forwardExplain the concept of data quality in the context of data warehousing. What are common data quality issues, and how can they be addressed?arrow_forward
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