DATA WAREHOUSING CONCEPTS PDF
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.
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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.
What is a Data Warehouse?
Data Warehouse Architecture Systems in operation Most businesses find their corporate data assets fragmented across disparate application systems which are running on various technical platforms in multiple geographical locations.
This heterogeneity in data structure does not support good decision making as there is monotony which leads to the loss of data quality.
As a current trend for businesses, integration of operational data from various organizations has led to the development of mutually co existent business partners.
For the same, sharing of consolidated historical data among such business partners can improve their business prospects and profits.
The databases which are operational in an organization generally deal with a relational data view with a primary focus of data entry and do not support the consolidation of data, the generalization of data, and analytics. Data Warehousing is the solution for such business requirements wherein data is consolidated and integrated from the various operational databases of an organization which runs on several technical platforms across different physical locations.
Data Warehousing Introduction and PDF tutorials
Transfer of all kinds of consolidated data is possible through ETL technology. Data is moved from one component of the model to another, all of which are accessible by decision makers.
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. 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.
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.
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 not directly update the data warehouse.
In OLTP systems, end users routinely issue individual data modification statements to the database. The OLTP database is always up to date, and reflects the current state of each business transaction. Schema design Data warehouses often use denormalized or partially denormalized schemas such as a star schema to optimize query performance.
Typical operations A typical data warehouse query scans thousands or millions of rows. For example, "Find the total sales for all customers last month. For example, "Retrieve the current order for this customer. This is to support historical analysis. OLTP systems usually store data from only a few weeks or months.
The OLTP system stores only historical data as needed to successfully meet the requirements of the current transaction.It is clear that the point-solution-independent data mart is not necessarily a bad thing, and it is often a necessary and valid solution to a pressing business problem, thus achieving the goal of rapid delivery of enhanced decision support functionality to end users.
A common way of introducing data warehousing is to refer to the characteristics of a data warehouse as set forth by William Inmon:.
Data warehouses must put data from disparate sources into a consistent format. The data warehouse becomes the common information resource for decisional purposes throughout the organization.
The time dimensions would not be conformed if one time dimension were weeks and the other time dimension, a fiscal quarter. 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.
One major difference between the types of system is that data warehouses are not usually in third normal form 3NF , a type of data normalization common in OLTP environments. Data Warehouse Architectures Data warehouses and their architectures vary depending upon the specifics of an organization's situation.
Cannot actively monitor changes in a data.
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