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Questions and Answers
What is a benefit of using multiple facts tables in a star schema?
What is a benefit of using multiple facts tables in a star schema?
- They can improve performance. (correct)
- They reduce the amount of data stored.
- They simplify data retrieval processes.
- They eliminate the need for dimension tables.
What is a factless fact table typically used for?
What is a factless fact table typically used for?
- Providing aggregated sales data.
- Tracking events. (correct)
- Storing detailed transactional data.
- Storing historical product information.
How do conformed dimensions benefit a data warehouse?
How do conformed dimensions benefit a data warehouse?
- They facilitate data integration across multiple fact tables. (correct)
- They restrict dimensions to single fact tables.
- They enhance data redundancy.
- They normalize data within a single schema.
What does the normalization of multivalued dimensions involve?
What does the normalization of multivalued dimensions involve?
What characterizes a fixed-depth hierarchy in data warehousing?
What characterizes a fixed-depth hierarchy in data warehousing?
What is the primary purpose of surrogate keys in data warehousing?
What is the primary purpose of surrogate keys in data warehousing?
Which of the following best describes a fact table?
Which of the following best describes a fact table?
In the context of dimensional modeling, what does a helper table accomplish?
In the context of dimensional modeling, what does a helper table accomplish?
What is the main purpose of normalization in data transformation?
What is the main purpose of normalization in data transformation?
Which transformation process explicitly combines data from different sources?
Which transformation process explicitly combines data from different sources?
In the context of data aggregation, what does it mean to transform data from detailed to summary level?
In the context of data aggregation, what does it mean to transform data from detailed to summary level?
Selection in data transformation is best described as what?
Selection in data transformation is best described as what?
Which transformation method uses a logical expression or formula?
Which transformation method uses a logical expression or formula?
What is the function of using a table lookup in data transformation?
What is the function of using a table lookup in data transformation?
Which of the following statements correctly describes multifield transformations?
Which of the following statements correctly describes multifield transformations?
Which of the following best illustrates the notion of granularity in data warehousing?
Which of the following best illustrates the notion of granularity in data warehousing?
Why are surrogate keys preferred for dimension tables?
Why are surrogate keys preferred for dimension tables?
What is the advantage of having a finer granularity in a fact table?
What is the advantage of having a finer granularity in a fact table?
Which of the following statements about the size of a fact table is true?
Which of the following statements about the size of a fact table is true?
What level of detail does transactional grain refer to in a fact table?
What level of detail does transactional grain refer to in a fact table?
How long is the natural duration recommended for a database?
How long is the natural duration recommended for a database?
What does the duration of older data typically signify for databases?
What does the duration of older data typically signify for databases?
In web-based commerce, what is considered the finest granularity?
In web-based commerce, what is considered the finest granularity?
What is NOT a reason to employ surrogate keys in dimension tables?
What is NOT a reason to employ surrogate keys in dimension tables?
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Study Notes
Variations of the Star Schema
- Multiple Facts Tables: Enhance performance by storing facts for various dimension combinations, typically associated with conformed dimensions.
- Factless Fact Tables: Contain no non-key data; focus solely on foreign keys for tracking events and inventory coverage.
Conformed Dimensions
- Conformed dimensions link multiple fact tables across distinct star schemas, ensuring uniformity in data representation.
Normalizing Dimension Tables
- Multivalued Dimensions: Introduce normalization to create associative entities for facts qualified by multiple values.
- Hierarchies: Can represent natural fixed-depth structures; design may involve either a denormalized single table or a normalized series of 1:N tables.
Surrogate Keys
- Employ surrogate keys in dimension tables to avoid issues with changing business keys, providing simplicity and consistency across data storage.
Grain of the Fact Table
- Determines the level of detail; finer granularity offers better analytics but increases complexity with more dimension tables and rows.
- Web-based commerce often utilizes clicking data as the finest level of granularity.
Duration of the Database
- Typically structured around a natural duration of 13 months or 5 quarters, though financial sectors may require longer periods due to challenges in data sourcing and cleansing.
Size of Fact Table
- Influenced by the number of dimensions and the granularity; total row count is calculated based on possible dimension values.
- Example calculation: 1,000 stores, 5,000 products, and 24 months may yield up to 120 million rows.
Record Level Transformation Functions
- Selection: Dividing data based on criteria.
- Joining: Consolidating data from diverse sources into one table/view.
- Normalization: Breaking down relations with anomalies for structured relationships.
- Aggregation: Summarizing detailed data into higher-level insights.
Transformation Application
- Single-Field Transformations: Can use algorithms or lookups to manage data efficiently.
- Multifield Transformations: Involves combinations where multiple sources lead to a single target or one source connects to numerous targets.
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