Mis430 Data Warehousing Final

An outrigger is an exception to the rule that allows snow flaking. Snow flaking is a technique used in database design where a large table is broken down into smaller, more manageable tables. However, an outrigger is a separate table that is not directly related to the main table but is linked to it through a foreign key. This allows for additional information or attributes to be stored without altering the structure of the main table. Therefore, an outrigger is an exception to the snow flaking rule as it introduces an additional table in the database design.

Normalization is a process in database design that organizes data into separate tables to reduce data duplication and increase data integrity. By breaking down data into smaller, more manageable tables and establishing relationships between them, normalization helps to eliminate redundant data and ensures that each piece of information is stored in only one place. This enhances data integrity by minimizing the risk of inconsistencies or discrepancies in the database. Therefore, normalization is the correct answer as it effectively reduces data duplication and improves data integrity in a database.

The given characteristics describe a fact table in a data warehouse. Fact tables are used to store measures or metrics, such as sales revenue or quantity sold, and they are typically used for aggregation purposes. Fact tables contain many records and are primarily composed of numeric data. Therefore, the correct answer is "fact."

The given correct answer is "snowflake." In the context of database design, a snowflake schema is a normalized version of the star schema. In a snowflake schema, dimension tables are partially or fully normalized, meaning they are broken down into multiple tables to reduce redundancy and improve data integrity. However, this normalization can lead to more complex queries and slower performance compared to the star schema, which is why it is not generally recommended.

A type 2 dimension is a dimension that tracks historical changes over time. In order to implement a type 2 dimension, a surrogate key is necessary. A surrogate key is a unique identifier assigned to each record in the dimension table. It is used to track changes and maintain historical data by creating a new record with a new surrogate key whenever a change occurs in the dimension. Without a surrogate key, it would be difficult to accurately track and manage the historical changes in a type 2 dimension.

The given correct answer is "star". In the dimensional data model, the schema is arranged in a star formation, where the fact table is placed in the middle and surrounded by dimension tables. Each dimension table has a one-to-many relationship with the fact table. This star schema is commonly used in data warehousing and allows for efficient querying and analysis of data.

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The given answer "first" does not seem to be related to the provided explanation. The explanation mentions characteristics of a normal form, such as data being atomic, no repeating groups, and having a primary key. However, it does not provide any specific information or context to determine why "first" would be the correct answer. Without further information, it is not possible to generate a meaningful explanation for this question.

The given explanation describes the concept of normal form in database design. It states that in a normal form, relationships between tables are defined without any transitive dependency. Additionally, it mentions that every attribute in a normal form depends on the primary key. The correct answer, "third," aligns with this explanation.

The term "roll up" refers to the process of summarizing or consolidating detailed data into higher-level categories or groups. In the context of analyzing data, it is the opposite of "drill down" or "drilling down," which involves examining data at a more detailed or granular level. "Rolling up" allows for a broader view or perspective of the data, providing a higher-level summary that can be useful for decision-making or reporting purposes.

The second option is the correct answer because it accurately describes the properties of a table in normal form. Normal form refers to the organization and structure of data in a relational database. In a normal form, there are no duplicate rows of data, and any duplicate data is moved to another table. Additionally, every attribute in the table depends on the entire key, meaning that each attribute is uniquely identified by the key.

The given statement describes the characteristics of a relational database. In a relational database, data is organized into tables and relationships between tables are established using primary and foreign keys. Referential integrity ensures that these relationships are maintained and data remains consistent. Therefore, the correct answer is "relational."

The term "periodic" refers to a system or process that occurs at regular intervals or time periods. In the given context, "periodic" suggests that the snapshot or inventory count is conducted periodically, with a specific time period designated for each count. This means that there will be one row of data for each item in the inventory for each time period in which the count is conducted.

The correct answer is "three". This is because the given scenario involves a change in the location attribute in the source system, and the requirement is to keep track of both the old and new values. This implies that a new column, "O location" (old location), is added to store the previous value, and a column, "N location" (new location), is added to store the updated value. Therefore, the total number of dimensions in this scenario is three.

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The given answer "factless" suggests that the fact table does not contain any measures, only surrogate keys. In a data warehouse, a fact table typically contains quantitative data or measures that can be analyzed, such as sales revenue or customer count. However, in some cases, a fact table may not have any measures and only consist of surrogate keys, which are unique identifiers for the dimensions. This type of fact table is referred to as "factless" as it lacks any measurable data.

A relational database is likely to have more tables because it follows the principles of normalization. Normalization is the process of organizing data in a database to eliminate redundancy and improve data integrity. This involves breaking down data into smaller, more manageable tables and establishing relationships between them. By doing so, data redundancy is minimized, and data integrity is improved. Therefore, a relational database with a normalized structure is likely to have more tables compared to a non-relational database.

The characteristics mentioned in the question, such as asking a question, slicing and dicing the data, using numeric data as a buffer, and having more columns or fields, all point towards dimension tables. Dimension tables in data analysis are used to provide context and describe the dimensions or attributes of the data being analyzed. They help in organizing and categorizing data for better analysis and understanding.

The given answer "two" suggests that the data warehouse should preserve the history of changes for the same attribute. This means that if there are multiple changes made to an attribute, all of those changes should be stored in the data warehouse. In the example provided, the attribute "Location" has two different values for the same customer, indicating that the history of changes for this attribute is being preserved.

The given answer "transaction" is the correct answer because a transaction refers to a single unit of work or a series of actions that are treated as a single unit. In the context of the fact table, orders, work requests, call records, etc., each row represents a specific action or event, which can be considered as a transaction. Therefore, the fact table or other related tables would contain one row for each transaction or action that occurred.

Derived attributes are not stored in the relational tables but are calculated or derived from other attributes in the table. These attributes are determined based on the values of other attributes and can be calculated whenever needed. Unlike regular attributes, derived attributes do not require separate storage as their values can be obtained through calculations or formulas. Therefore, derived attributes are not physically stored in the tables but are dynamically derived when required.

The sum aggregation type is used to add up fields. This means that it calculates the total sum of the values in a specific field. It is commonly used in data analysis to obtain the total sum of numerical values in a dataset. By using the sum aggregation, one can quickly determine the cumulative value of a particular field, providing valuable insights into the data.

The term "grain" refers to the level of detail or granularity in a fact table. It represents the combination of the lowest level of the fact table, such as store, month, and state. In other words, the grain determines the specific dimensions or attributes that are included in the fact table. For example, if the grain is set to store, month, and state, the fact table will include data at that level of detail.

A data warehouse uses more space because it is denormalized. In a denormalized data warehouse, redundant data is stored to improve query performance and simplify data retrieval. This redundancy leads to an increase in the amount of storage space required compared to a normalized database, where data is organized more efficiently to minimize redundancy.

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The term "conformed" in this context refers to the concept of having shared dimensions between multiple fact tables in a data warehouse. When two or more fact tables share the same dimensions, it means that these dimensions have the same structure, attributes, and meaning across all the tables. This conformity ensures consistency and allows for accurate analysis and reporting across different fact tables. By conforming dimensions, the data warehouse can effectively integrate data from various sources and provide a holistic view of the business operations.

The given answer "users" is correct because the sentence is discussing different types of information delivery, such as tourists and operators. In this context, users refer to the individuals or groups who utilize or benefit from these different types of information delivery.

The term "composite" refers to the process of breaking up attributes into atomic columns. In a database, attributes are typically organized into tables and columns. When an attribute is composite, it means that it is made up of multiple sub-attributes or atomic columns. This allows for more flexibility and efficient data storage and retrieval. By breaking up attributes into atomic columns, it becomes easier to manipulate and analyze the data.

The given correct answer is "surrogate". In a data warehouse, a surrogate key is a unique identifier assigned to a dimension table that is stored directly in the fact table. This surrogate key is used to establish relationships between the fact table and the dimension table. It provides a way to efficiently join and query data in the data warehouse. By using surrogate keys, the dimension table can be updated without affecting the fact table, ensuring data integrity and maintaining historical accuracy.

The correct answer is "primary clustered". In a primary clustered index, the data is physically stored in the same order as the index. This means that the rows in the table are physically sorted based on the values in the indexed column(s). This can improve the performance of queries that involve range scans or sorting operations, as the data is already organized in the desired order. Having a primary clustered index can also help reduce disk I/O and improve data retrieval speed.

In database design, the concept of atomicity refers to breaking down composite attributes into single attributes. This means that instead of storing multiple pieces of data within a single column, each piece of data is stored in its own separate column. By doing this, data is not duplicated within columns, which helps to ensure data integrity and avoid redundancy. Therefore, the given answer "atomic" is correct as it accurately describes the process of breaking down composite attributes into single attributes.

Querying is easier in a data warehouse that is not normalized because normalization involves breaking down data into multiple tables to minimize redundancy. However, this can make querying more complex as it requires joining multiple tables to retrieve the desired information. In a denormalized data warehouse, data is stored in a single table or fewer tables, making it easier for users to query and retrieve data without the need for complex joins.

Views in a database are virtual tables that are created based on the result of a query. They act like tables because they can be queried and manipulated like regular tables, but they are not physical tables themselves. Views restore intrusiveness to a database by allowing users to access and query specific data without directly accessing the underlying tables. They also ease user querying by providing a simplified and customized view of the data. Views can limit access to sensitive information by only displaying certain columns or rows, enhancing data security. Additionally, views abstract the database implementation and allow for changes to be made to the underlying tables without affecting the view's structure or functionality.

The given answer, "hierarchy," is correct because the examples provided show a hierarchical structure. The first example shows a hierarchy where a year is broken down into months, and then further broken down into dates. The second example shows a similar hierarchy where a year is broken down into weeks, and then further broken down into dates. In both cases, the structure follows a hierarchical pattern where each level is broken down into smaller units. Therefore, "hierarchy" is the appropriate term to describe this pattern.

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In a normalized database, attributes that have multiple values are not allowed. This is because having multiple values in a single attribute violates the principle of atomicity, which states that each attribute should have a single value. Therefore, to handle multi-valued attributes, it is necessary to use another table. This additional table will have a foreign key that links it to the main table, allowing for a one-to-many relationship and avoiding data redundancy.

Normalization is important outside the data warehouse because it helps maintain data integrity, reduce storage space, and eliminate duplication. By normalizing the data outside the data warehouse, we can ensure that the data is organized and structured efficiently, minimizing the chances of data inconsistencies and anomalies. Additionally, normalization helps in optimizing storage space by reducing redundant data and improving data retrieval performance.

The given characteristics describe the components of a data warehouse. These components include the source data, which comes from transactional databases or data marts. The data staging component involves filtering and cleaning the data before it is stored. The data storage component is where the data is stored in the data warehouse. Finally, the information delivery component determines how users can access the data, such as through reports or queries.

The explanation for the given correct answer "rotation" is that it refers to the act of looking at data in a different way by changing the way rows in a column are displayed. This suggests that the data is being rotated or reorganized to provide a different perspective or view.

The term "accumulating" suggests that the given answer is referring to a process of gathering or collecting data over time. In this context, it implies that the system or application being discussed is tracking the lifecycle of something, such as a product or a project, and maintaining a record of each lifecycle event in a row. This could be useful for analyzing trends, monitoring progress, or generating reports based on the accumulated data.

The term "drill down" refers to the process of analyzing data or information at a more detailed or granular level. It involves breaking down complex information into smaller parts to gain a deeper understanding or uncover more specific insights. This term is commonly used in data analysis, business intelligence, and reporting to explore data hierarchies and navigate from a higher-level overview to a more specific level of detail. By drilling down, analysts can identify patterns, trends, and outliers that may not be apparent at a higher level of aggregation.

The given correct answer "multi valued" suggests that the attribute in question is capable of having multiple values for a single entity. This means that the attribute can have more than one value associated with it, and these values can be stored in a separate table. This allows for a more efficient and organized way of representing the data, as each value can be stored in a separate row in the table, linked to the main entity through a foreign key.

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Slice and dice is a technique used in data analysis to filter and manipulate data in a cube. It involves selecting specific dimensions or attributes of the data to focus on and then drilling down or slicing the data further to gain more detailed insights. By slicing, we are filtering the data by selecting a specific subset of the dimensions, while dicing involves filtering the data by selecting multiple subsets of dimensions. Therefore, slice and dice is the correct answer for the given question.

The given correct answer suggests using the "count distinct" aggregation type to determine the number of distinct order keys. This aggregation function counts the number of unique values in a specific column, in this case, the order keys. By applying the "count distinct" function to the order keys, we can obtain the total count of unique order keys, which corresponds to the number of orders.

The given answer "aggregation" is correct because the characteristics mentioned, such as "Sum, Avg, count count distinct," are commonly associated with aggregation functions in data analysis or database operations. Aggregation involves combining multiple values into a single value, often using functions like sum, average, or count. These functions are used to summarize or calculate values from a larger set of data, making "aggregation" the appropriate term to describe the mentioned characteristics.

A degenerate dimension is a dimension that does not have a separate dimension table and is instead stored directly in the fact table. This means that the dimension's attributes are stored alongside the measures in the fact table, eliminating the need for a separate dimension table. This can be useful when the dimension has a single attribute or when the attribute is highly correlated with the fact table.

The given correct answer is "mining." This suggests that the applications mentioned in the question are related to data mining. Data mining involves analyzing large sets of data to discover patterns, relationships, and insights that can be used for various purposes such as fraud detection, risk management, customer segmentation, and market basket analysis.

The given characteristics describe the properties of a data warehouse. In a data warehouse, tables are often normalized to eliminate redundancy and improve data integrity. It consists of Fact and Dimension tables, where Fact tables store the measurable data and Dimension tables provide context and descriptive attributes. Additionally, data in a data warehouse is typically read-only, meaning it is not updated or modified frequently, but rather used for analysis and reporting purposes.