Data Warehousing & Data Management Quiz

A data object is a representation of an entity, such as a customer, product, or order. It encapsulates all the relevant information about that entity.

Attributes are characteristics or properties that describe a data object. Each attribute represents a specific aspect of the object, such as name, age, or price.

The mode is the value that appears most frequently in a dataset. It indicates the most common observation in the data.

A boxplot is a graphical representation of a dataset's five-number summary. It displays the minimum, first quartile, median, third quartile, and maximum values of the data.

A histogram is a graphical representation of data where the x-axis represents the values and the y-axis represents their corresponding frequencies. The bars in the histogram depict the frequency distribution of the data.

An ordinal variable is a type of variable that has a natural order or ranking. It can be discrete, having a finite number of values, or continuous, with an infinite number of values within a range.

A document can be represented as a data object with thousands of attributes that capture its content, structure, and other relevant information.

A scatter plot is a graph that depicts the relationship between two variables using points. Each point represents a pair of values, plotted as coordinates in a two-dimensional plane.

Removing irrelevant attributes from the dataset to reduce its size while preserving essential information.

An attribute's effect on a class doesn't depend on other attribute values.

A data warehouse's primary purpose is to support decision-making by storing large amounts of historical data.

A multidimensional database, also known as a relational database management system (RDBMS), is designed to store and analyze data with multiple dimensions, making it ideal for data warehousing.

Data warehouses typically use relational database management systems as their core architecture, providing structured data storage and retrieval.

The primary source of data for data warehouses is often operational data stores (ODS), where transactional data is collected and processed.

Filtering is a data transformation process that selectively eliminates data based on specific criteria.

Multidimensional databases are designed to overcome limitations of relational models, providing better support for data warehousing and analysis.

The level of detail at which data is recorded.

A fact is fully additive if it can be summed up across any of its dimensions.

A fact is partially additive if it can be summed up across some of its dimensions, but not all.

A fact is non-additive if it cannot be summed up across any of its dimensions.

The property that all attributes of an entity depend on the entire primary key.

A database query language used to access and manipulate data.

A type of data processing that focuses on analyzing large amounts of data to gain business insights.

Data that can be categorized into dimensions and measures.

A process involving loading data into a data warehouse and creating necessary indexes for efficient data retrieval.

Transforming data from a detailed level to a summarized level, essentially condensing information for easier analysis.

A type of data conversion that involves changing data from one field into multiple fields.

A database design where data is organized into fact tables and dimension tables. The relationship between the fact table and dimension tables is one-to-many.

Fact tables in a star schema are fully normalized, meaning data is organized efficiently and avoids redundancy.

Business Intelligence (BI) and data warehousing are used for forecasting future trends, data mining to extract insights from data, and analyzing large volumes of data.

The data administration subsystem is responsible for managing data in a warehouse, but it does not directly create, change, or delete information.

Queryable change data is the most common source for refreshing data in a warehouse. This means data changes are tracked and can be used to update the warehouse.

A subject-oriented, integrated, time-variant, nonvolatile collection of data used to support management decisions.

Data warehouses are designed for read-only operations. Data is extracted, transformed, and loaded (ETL) for analytical purposes.

Decision Support System (DSS). It refers to systems that help users make informed decisions by providing analytical capabilities.

Data warehouses are subject-oriented, time-variant, and integrated. This means they organize data around business topics, track changes over time, and combine data from multiple sources.

Data warehouses typically store data for an extended period, often spanning from 5 to 10 years, to analyze trends and patterns over time.

Online Transaction Processing (OLTP) systems handle real-time transactions and update data frequently. They are used for day-to-day business operations.

Metadata describes the data stored in the data warehouse. It provides information about the data's structure, meaning, and origin.

Data warehouse database servers are the central component, storing and managing the data for analytical queries.

A data point that significantly deviates from the general pattern or behavior of the dataset. It can be caused by errors, unusual events, or simply the inherent variability of the data.

The process of collecting, storing, and organizing data from various sources in a way that makes it readily available and useful for analysis.

The process of reducing the size of a dataset without losing significant information. This can be achieved by techniques like data aggregation, feature selection, or dimensionality reduction.

A type of data transformation that involves converting data from a continuous scale to a discrete scale. This can be useful for simplifying data analysis or for creating categorical features.

A database designed for analytical reporting and decision making. It typically stores historical data from multiple sources, making it suitable for exploring trends and patterns over time.

A type of data warehouse designed for specific business needs, often with a smaller scope and faster query performance.

A data structure used in data warehousing that represents data in a multidimensional fashion, allowing for slice-and-dice analysis. It consists of a fact table in the center surrounded by dimension tables.

Data that describes other data. It provides information about the structure, content, and context of a dataset. This can be used for metadata management, data governance, and data quality control.