DATA WAREHOUSING CONCEPTS PDF

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PDF | In recent years, it has been imperative for organizations to This book deals with the fundamental concepts of data warehouses and. warehousing. Audience. This tutorial will help computer science graduates to understand the basic-to-advanced concepts related to data warehousing. CompRef8 / Data Warehouse Design: Modern Principles and Methodologies . concepts, such as customers, products, sales, and orders.


Data Warehousing Concepts Pdf

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1 Data Warehousing Concepts. This chapter provides an overview of the Oracle data warehousing implementation. It includes: What is a Data Warehouse?. DATA WAREHOUSE CONCEPTS. A fundamental concept of a data warehouse is the distinction between data and information. Data is composed of observable . Business Intelligence. Slides kindly borrowed from the course. “Data Warehousing and Machine Learning”. Aalborg University, Denmark. Christian S. Jensen.

Integrated Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format.

Data Warehousing Introduction and PDF tutorials

They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated.

Nonvolatile Nonvolatile means that, once entered into the data warehouse, data should not change. This is logical because the purpose of a data warehouse is to enable you to analyze what has occurred. Time Variant A data warehouse's focus on change over time is what is meant by the term time variant.

In order to discover trends in business, analysts need large amounts of data. This is very much in contrast to online transaction processing OLTP systems, where performance requirements demand that historical data be moved to an archive.

Data warehouses and OLTP systems have very different requirements.

Here are some examples of differences between typical data warehouses and OLTP systems: Workload Data warehouses are designed to accommodate ad hoc queries. You might not know the workload of your data warehouse in advance, so a data warehouse should be optimized to perform well for a wide variety of possible query operations.

Data Warehouse Architecture With Diagram And PDF File

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.

Apart from the transfer of data which involves extraction and loading, ETL is also responsible for transforming of inconsistent data, cleansing and filtering of data.

Owing to such critical importance, ETL scheduling is critical as a single failure would disturb the entire process. Next in line, A Staging Area Component Utilizing the ETL technology, once data from source databases is copied, it is moved into a temporary location called a Data warehouse staging area.

Data Warehouse Architectures

The primary reason for the existence of a staging area is to ensure that all needed data is consolidated before it can be integrated into the main components of a Data Warehouse. In an active business, there exist many limitations in the hardware, network resource as well as differences in business cycles and data processing cycles which makes it a challenge to extract all the data from the databases simultaneously.

Similarly, extracting data is also affected by time zones which change greatly with geographical location. The Data Warehouse Server From the staging area by means of ETL, the data is then integrated with the various internal and external operational databases of the organization which operate across the globe.

This leads to a humongous collection of detailed data. For example, the data of every sale ever recorded by a business would be convoluted which enables it to be statistically analyzed very efficiently.

Front-end Data marts With assistance from the ETL technology, operations of transferring data from the warehouse to a data mart is done. Extracted data is represented on one or several Data Marts which enables it to be accessed by the organizations reviewers. The Data Marts often showcase a multi-dimensional view of extracted data with the help of front-end Data Warehousing OLAP Tools will be used to visualize the analyzed data or information.In most instances, however, the data mart is a physically separate store of data and is normally resident on a separate database server, often on the local area enterprises relational OLAP technology which creates highly denormalized star schema relational designs or hypercubes of data for analysis by groups of users with a common interest in a limited portion of the database.

There are two approaches to data warehousing:

Attempts to meet both decisional and operational information needs through the same system or through the same system architecture merely increase the brittleness of the IT architecture and will create system maintenance nightmares. All these type of data marts, called dependent data marts because their data content is sourced from the data warehouse, have a high value because no matter how many are deployed and no matter how many different enabling technologies are used, the different users are all accessing the information views derived from the same single integrated version of the data.

This gives insight about needed steps to more efficiently market a given product.

See Also: A data warehouse is structured to support business decisions by permitting you to consolidate, analyse and report data at different aggregate levels. To address data integration issues associated with data marts, the recommended approach proposed by Ralph Kimball is as follows. Data warehouses are designed to accommodate ad hoc queries.

Adding new data takes lot of time and includes cost. Instead, historical data are loaded and integrated with other data in the warehouse for quick access.

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