Data Warehousing Concepts Quiz

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Questions and Answers

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?

Providing aggregated sales data.

Tracking events. (correct)

Storing detailed transactional data.

Storing historical product information.

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?

Creating a table for an associative entity between dimensions. Signup and view all the answers

What characterizes a fixed-depth hierarchy in data warehousing?

It allows for a natural organization of data at various aggregation levels. Signup and view all the answers

What is the primary purpose of surrogate keys in data warehousing?

To replace natural keys for improved performance. Signup and view all the answers

Which of the following best describes a fact table?

A table consisting of foreign keys associated with dimensions and performance metrics. Signup and view all the answers

In the context of dimensional modeling, what does a helper table accomplish?

It implements a many-to-many relationship between facts and dimensions. Signup and view all the answers

What is the main purpose of normalization in data transformation?

To produce smaller, well-structured relations Signup and view all the answers

Which transformation process explicitly combines data from different sources?

Joining Signup and view all the answers

In the context of data aggregation, what does it mean to transform data from detailed to summary level?

To present a higher-level overview of the data Signup and view all the answers

Selection in data transformation is best described as what?

The process of partitioning data based on criteria Signup and view all the answers

Which transformation method uses a logical expression or formula?

Algorithmic Signup and view all the answers

What is the function of using a table lookup in data transformation?

To utilize a separate table based on source record codes Signup and view all the answers

Which of the following statements correctly describes multifield transformations?

They can transform one source into multiple targets or vice versa. Signup and view all the answers

Which of the following best illustrates the notion of granularity in data warehousing?

The detailed level of transactional data versus summarized data Signup and view all the answers

Why are surrogate keys preferred for dimension tables?

They are simpler and shorter than business keys. Signup and view all the answers

What is the advantage of having a finer granularity in a fact table?

It allows better market basket analysis capability. Signup and view all the answers

Which of the following statements about the size of a fact table is true?

It is determined by the product of the number of possible values for each associated dimension. Signup and view all the answers

What level of detail does transactional grain refer to in a fact table?

The most detailed level of data captured. Signup and view all the answers

How long is the natural duration recommended for a database?

13 months or 5 quarters. Signup and view all the answers

What does the duration of older data typically signify for databases?

It becomes more difficult to source and cleanse. Signup and view all the answers

In web-based commerce, what is considered the finest granularity?

A click. Signup and view all the answers

What is NOT a reason to employ surrogate keys in dimension tables?

They maintain a relationship with business keys. Signup and view all the answers

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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Description

Test your knowledge on advanced data warehousing concepts including star schema variations and conformed dimensions. Explore techniques like normalizing dimension tables and the usage of surrogate keys. This quiz covers both theoretical understanding and practical applications in data modeling.