24 Questions
Which data modeling technique is used for designing a data warehouse?
Data modeling
What is a key characteristic of a data warehouse?
Non-volatility
What is the primary purpose of a data warehouse?
Analyzing data
What is the difference between normalization and denormalization?
Normalization divides data into multiple tables, while denormalization combines data into a single table.
What is the key difference between a data warehouse and a database?
A data warehouse is designed for analyzing data, while a database is designed for recording data.
What is the purpose of data loading in a data warehouse?
To insert new data
Which processing technique is used in a data warehouse?
OLAP
What is a characteristic of data warehouse tables and joins?
Denormalized and simpler
What is the primary purpose of a data mart?
To support business-line specific reporting and analysis
What is a key characteristic of an enterprise data warehouse?
It integrates data from all business units
What is the main advantage of using a virtual data warehouse?
It allows users to access data as if it was stored in one data warehouse
What is the primary goal of data integration in a data warehouse?
To provide a unified view of the organization
What is a key characteristic of a data warehouse?
It is subject-oriented, delivering information about a specific subject
What is the main difference between a data mart and an enterprise data warehouse?
A data mart is focused on a specific business area, while an enterprise data warehouse spans the entire organization
Why do external applications or reporting tools typically query data marts instead of enterprise data warehouses?
Because data marts are more specialized and contain relevant data for a specific business area
What is a distinct feature of a data warehouse compared to operational systems?
It is time-variant, storing historical data
What is the primary purpose of ETL processes in data warehousing?
To transform and load data into a data warehouse
What is the characteristic of data in an enterprise data warehouse?
Data is captured at the lowest level of detail
What is the purpose of data marts in a data warehouse architecture?
To provide departmental views of information
What is the design used in data marts?
Dimensional design
What is the process of transforming data into a specific standard format?
Data transformation
What is the term used to describe the process of consolidating data from various sources?
Data integration
What is the characteristic of an enterprise data warehouse?
It is a centralized repository of data
What is the purpose of data analysis applications in a data warehouse architecture?
To analyze data in a data mart
Study Notes
Data Warehouse Models
- There are three types of data warehouse models:
- Data mart: used for business-line specific reporting and analysis, aggregating data from a range of source systems relevant to a specific business area.
- Virtual data warehouse: a set of separate databases that can be queried together, providing access to all the data as if it was stored in one data warehouse.
- Enterprise data warehouse: contains aggregated data that spans the entire organization, integrating data from all business units.
Characteristics of Data Warehouse
- Subject-Oriented: delivers information about a specific subject, rather than a company's current operations.
- Integrated: integrates data from multiple sources, storing it in a unified manner.
- Time-variant: stores data with a specific time period, providing information from a historical perspective.
- Non-volatile: data is permanent, and previous data is not replaced, omitted, or deleted when new data is inserted.
Data Warehouse vs. Database
- Data warehouse: serves as an information system containing historical and communicative data from one or several sources, used for analyzing data, subject-oriented, and uses Online Analytical Processing (OLAP).
- Database: an amalgamation of related data, used for recording data, application-oriented, and uses Online Transactional Processing (OLTP).
Normalization and Denormalization
- Normalization: dividing data into multiple tables to reduce data redundancy and inconsistency, achieving data integrity.
- Denormalization: combining data into a single table to make data retrieval faster.
Inmon vs. Kimball
- Inmon's data warehouse architecture: corporate applications (transaction systems) provide data to the data warehouse, which is then divided into multiple data marts based on different department needs.
- Kimball's approach is not mentioned in the text.
Functions of Data Warehouse
- Data cleaning
- Data integration
- Data mapping
- Data extraction
- Data transformation
- Data loading
- Data refreshing
Inmon's Data Warehouse Architecture
- Corporate Applications (transaction systems) collect data from business transactions and store it in various forms.
- ETL (Extract, Transform, Load) process brings data from transaction systems, consolidates and transforms it, and loads it into an enterprise data warehouse (EDW).
- Enterprise data warehouse is a central element, capturing data at the lowest level of detail, and storing it in a relational database.
- Data marts are departmental views of information with subject-oriented data, taking data from the enterprise data warehouse, and using dimensional design for analysis.
This quiz covers different data warehouse models, including data mart, virtual data, and enterprise data warehouse, focusing on their usage and characteristics.
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