Data Modeling Quiz

Redundancies can compromise the consistency of the database and lead to anomalies.

An update anomaly occurs when changing one piece of data requires multiple changes in different places.

Normalization helps prevent anomalies by reducing redundancies and ensuring data is stored in a structured and consistent manner.

Relational, multidimensional, and semantic

To structure communication between users and developers and as a basis for database design

Redundant information can be stored

Compromise the consistency and can lead to anomalies.

If the department number changes, several tuples must be changed at the same time.

Normalization helps avoid redundancies and inconsistencies.

Star and snowflake schemas allow performance-oriented modeling of multidimensional spaces by structuring data in a way that optimizes query performance.

Physical data models specify how data is physically stored, logical data models describe data on a logical level regardless of storage, and semantic data models depict data on a technology-neutral level.

The entity relationship model (ERM) is used in the conceptual phase of application development to structure communication between users and developers, and as a basis for database design in the implementation phase.

The purpose of normalization is to eliminate redundancies and inconsistencies in data, making it more efficient to manage.

Edgar F. Codd

In 1NF, a table row may only contain one attribute value and the attribute value must be atomic.

The purpose of normalization in database design is to eliminate redundancies and inconsistencies in data, making it more efficient to manage.

The first normal form (1NF) states that a table row may only contain one attribute value and the attribute value must be atomic.

The theory of normal forms was developed by Edgar F. Codd, the inventor of the relational database.

A relation is in the second normal form if it is in the first normal form and all non-key attributes depend functionally on the entire key.

A relation is in the third normal form if the second normal form exists and no functional dependencies exist between non-key attributes.

A new table must be created from those attributes that only depend on a part of the compound primary key.

The focus of the OLAP cube model is usually on business ratios as carriers of quantitative information.

Attributes are the characteristics that describe a dimension, while dimensions are sets of attributes that explain a specific aspect of the data.

The relationship hierarchy facilitates both the aggregation of data and navigation through the data in the OLAP cube model.

spreadsheet-style display

values of X

values of Y

An OLAP cube is a multi-dimensional array of data used for online analytical processing (OLAP), which is a computer-based technique of analyzing data to look for insights.

A cube is a multi-dimensional dataset, while a hypercube refers to a multi-dimensional dataset with more than three dimensions.

A slice is a subset of the data in an OLAP cube that is generated by selecting a specific value for one dimension and only showing the data associated with that value.

A hierarchy is a set of parent-child relationships in which a parent member summarizes its children.

The common operations in OLAP cube analysis include slice and dice, drill down, roll up, and pivot.

The slice operation involves picking a rectangular subset of a cube by choosing a single value for one of its dimensions, creating a new cube with one fewer dimension.

The roll-up operation involves summarizing the data along a dimension, using an aggregate function or a set of formulas.

The result is a spreadsheet-style display.

Values of X populate row 1.

Values of Y populate column A.

An OLAP cube is a multi-dimensional array of data used for online analytical processing (OLAP). It is a computer-based technique of analyzing data to look for insights.

A cube is a multi-dimensional dataset, while a hypercube refers to a dataset with more than three dimensions.

A slice is a subset of the data generated by selecting a specific value for one dimension and displaying only the data associated with that value.

The purpose of the "slice" operation is to pick a rectangular subset of a cube by choosing a single value for one of its dimensions, creating a new cube with one fewer dimension.

The purpose of the "dice" operation is to produce a subcube by allowing the analyst to pick specific values of multiple dimensions.

The purpose of the "drill down/up" operation is to allow the user to navigate among levels of data ranging from the most summarized (up) to the most detailed (down).

The purpose of the "roll-up" operation is to involve summarizing the data along a dimension, typically using an aggregate function or a set of formulas.

The result is a spreadsheet-style display.

Values of X populate row $1.

Values of Y populate column $A.

The purpose of OLAP is to analyze data and look for insights.

A cube is a multi-dimensional dataset that represents the data's dimensions.

Measures are represented as numbers in each cell of the cube, while dimensions are derived from dimension tables.

The purpose of the "drill down" operation is to allow the user to navigate among levels of data ranging from the most summarized to the most detailed.

The purpose of the "pivot" operation is to allow an analyst to rotate the cube in space to see its various faces.

In database theory, an OLAP cube is an abstract representation of a projection of an RDBMS relation. Given a relation of order N, consider a projection that subtends X, Y, and Z as the key and W as the residual attribute.

The common operations in OLAP cube analysis include slice and dice, drill down, roll up, and pivot.

to display values of g : ( X, Y ) → W at intersections of X-labeled columns and Y-labeled rows

to populate the row $1

to populate the column $A

OLAP is a technique used for analyzing data to look for insights.

A cube is a multi-dimensional dataset that can be considered a multi-dimensional generalization of a spreadsheet.

Measures represent some measure of the business, such as sales or profits, while dimensions represent the different aspects or categories by which the data is summarized, such as product or time. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables.

The four common operations in OLAP cube analysis are slice and dice, drill down, roll up, and pivot.

The purpose of the slice operation in OLAP cube analysis is to pick a rectangular subset of a cube by choosing a single value for one of its dimensions, creating a new cube with one fewer dimension.

The purpose of the dice operation in OLAP cube analysis is to produce a subcube by allowing the analyst to pick specific values of multiple dimensions.

The purpose of the drill down operation in OLAP cube analysis is to allow the user to navigate among levels of data ranging from the most summarized to the most detailed.

The purpose of the roll up operation in OLAP cube analysis is to involve summarizing the data along a dimension.

The star schema is the simplest style of data mart schema and is widely used in data warehouses and dimensional data marts.

The star schema consists of one or more fact tables referencing any number of dimension tables.

The star schema is more effective for handling simpler queries and provides a clear and intuitive structure for organizing data in a data warehouse.

Transaction, snapshot, accumulating snapshot

Simpler queries, simplified business reporting logic, query performance gains, fast aggregations, feeding cubes

Time, geography, product, employee, range

To ensure each row can be uniquely identified

The main difference is that the dimension table of the snowflake schema is maintained in the normalized form to reduce redundancy.

The advantages include easy maintenance and storage space savings. However, it may result in slower query response times and higher resource usage.

The decision should consider query performance, schema complexity, and data integrity requirements.