Exam schedule
1. What is a data warehouse?
A data warehouse is defined in this section as “a pool of data produced to support decision making.” This focuses on the essentials, leaving out characteristics that may vary from one DW to another but are not essential to the basic concept.
The same paragraph gives another definition: “a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management’s decision-making process.” This definition adds more specifics, but in every case appropriate: it is hard, if not impossible, to conceive of a data warehouse that would not be subject-oriented, integrated, etc.
2. How is a data warehouse different from a database?
Technically a data warehouse is a database, albeit with certain characteristics to facilitate its role in decision support. Specifically, however, it is (see previous question) an “integrated, time-variant, nonvolatile, subject-oriented repository of detail and summary data used for decision support and business analytics within an organization.” These characteristics, which are discussed further in the section just after the definition, are not necessarily true of databases in general—though each could apply individually to a given one.
As a practical matter most databases are highly normalized, in part to avoid update anomalies. Data warehouses are highly denormalized for performance reasons. This is acceptable because their content is never updated, just added to. Historical data are static.
3. What is an ODS?
Operational Data Store is the database from which a business operates on an on-going basis.
4. Differentiate among a data mart, an ODS, and an EDW.
An ODS (Operational Data Store) is the database from which a business operates on an ongoing basis.
Both an EDW and a data mart are data warehouses. An EDW (Enterprise Data Warehouse) is an all-encompassing DW that covers all subject areas of interest to the entire organization. A data mart is a smaller DW designed around one
A data warehouse is defined in this section as “a pool of data produced to support decision making.” This focuses on the essentials, leaving out characteristics that may vary from one DW to another but are not essential to the basic concept.
The same paragraph gives another definition: “a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management’s decision-making process.” This definition adds more specifics, but in every case appropriate: it is hard, if not impossible, to conceive of a data warehouse that would not be subject-oriented, integrated, etc.
2. How is a data warehouse different from a database?
Technically a data warehouse is a database, albeit with certain characteristics to facilitate its role in decision support. Specifically, however, it is (see previous question) an “integrated, time-variant, nonvolatile, subject-oriented repository of detail and summary data used for decision support and business analytics within an organization.” These characteristics, which are discussed further in the section just after the definition, are not necessarily true of databases in general—though each could apply individually to a given one.
As a practical matter most databases are highly normalized, in part to avoid update anomalies. Data warehouses are highly denormalized for performance reasons. This is acceptable because their content is never updated, just added to. Historical data are static.
3. What is an ODS?
Operational Data Store is the database from which a business operates on an on-going basis.
4. Differentiate among a data mart, an ODS, and an EDW.
An ODS (Operational Data Store) is the database from which a business operates on an ongoing basis.
Both an EDW and a data mart are data warehouses. An EDW (Enterprise Data Warehouse) is an all-encompassing DW that covers all subject areas of interest to the entire organization. A data mart is a smaller DW designed around one