Database Data Warehousing Guide. Contents. Previous · Next. Page 5 of .. This section introduces basic data warehousing concepts. It contains the following. In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse .. , , , , ISBN Jump up ^ "Data Mart Concepts". Oracle. Jump up ^ "OLTP vs. OLAP". Datawarehouse4u. 1. Data Warehousing Concepts. This chapter provides an overview of the Oracle implementation of data warehousing. Its sections include: What is a Data.


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oracle data warehousing concepts For example, a sales transaction can be broken up into facts such as the number of products ordered and the total price paid for the products, and into dimensions such as order date, customer name, product number, order ship-to and bill-to locations, and salesperson responsible for receiving the order.

A key advantage of a dimensional approach is that the data warehouse is easier for the user to understand and to use.

Data Warehouse - Fundamentals

Also, the retrieval of data from the data warehouse tends to operate very quickly. Facts oracle data warehousing concepts related to the organization's business processes and operational system whereas the dimensions surrounding them contain context about the measurement Kimball, Ralph Another advantage offered by dimensional model is that it does not involve a relational database every time.

Thus, this type of modeling technique is very useful for end-user queries in data warehouse. The model of facts and dimensions can also be understood as data cube.

Where the dimensions are the categorical coordinates in a multi-dimensional cube, while the fact is a value corresponding to the coordinates. The main disadvantages of the dimensional approach are the following: To maintain the integrity of facts and dimensions, loading the data warehouse with data from different operational systems is complicated.

It is difficult to modify the data warehouse structure if the organization adopting the dimensional approach changes the way in which it does business. Normalized approach[ edit ] In the normalized approach, the data in the data warehouse oracle data warehousing concepts stored following, to a degree, database normalization rules.

Tables are grouped together by subject areas that reflect general data categories e. The normalized structure divides data into entities, which creates several tables in a relational database.

When applied in large enterprises the result is dozens of oracle data warehousing concepts that are linked together by a web of joins.


Furthermore, each of the created entities is converted into separate physical tables when the database is implemented Kimball, Ralph The user community must be intimately involved in the data warehouse from design through implementation or else it will fail.

Generally data warehouses are denormalized structures.


A normalized database stores the greatest amount of data in the smallest amount of space, in a data warehouse we sacrifice storage space for speed through denormalization. A dyed in the wool OLTP designer may have difficulty in crossing over to the dark side of data warehousing design.

Many of the time-honored concepts are bent or completely broken when designing a data oracle data warehousing concepts.

The database must be designed with a DWH multi-functional profile in mind.

Data Warehousing Concepts

OLTP systems support only predefined operations. Your applications might be specifically tuned or designed to support only these operations. Data modifications A data warehouse is updated on a regular basis by the ETL process run nightly or weekly using bulk data modification techniques.

The end users of a data warehouse do oracle data warehousing concepts directly update the data warehouse. In OLTP systems, end users routinely issue individual data modification statements to the database.

  • Introduction to Data Warehousing Concepts
  • Data warehouse - Wikipedia
  • Oracle Data Warehouse DWH database tuning
  • Data warehouse
  • Descripción

The data in a data warehouse is typically loaded through an extraction, transformation, and loading ETL process from multiple data sources. Modern data warehouses are moving toward an extract, load, transformation Oracle data warehousing concepts architecture in which all or most data transformation is performed on the database that hosts the data warehouse.

Oracle Database 11g: Data Warehousing Fundamentals

It is important to note that defining the ETL process is a very large part of the design effort of a data oracle data warehousing concepts. Similarly, the speed and reliability of ETL operations are the foundation of the data warehouse once it is up and running.

Users of the data warehouse perform data analyses that are often time-related.