A Data
Warehouse (DW) is a database used for reporting. The data is offloaded from
the operational systems for reporting. The data may pass through an operational data store for
additional operations before it is used in the DW for reporting.
A data
warehouse maintains its functions in three layers: staging, integration, and
access. Staging is used to store raw data for use by developers
(analysis and support). The integration layer is used to integrate data
and to have a level of abstraction from users. The access layer is for
getting data out for users.
1. Ralph
Kimball's paradigm: Data warehouse is
the conglomerate of all data marts within the enterprise. Information is always
stored in the dimensional model.
Definition as Per Ralph Kimball: A data warehouse is a copy of transaction data
specifically structured for query and analysis.
His
Approach towards the Data warehouse Design is Bottom-Up. In the bottom-up
approach data marts are first
created to provide reporting and analytical capabilities for specific business processes.
2. Bill
Inman's paradigm: Data warehouse is one part of the overall business
intelligence system. An enterprise has one data warehouse, and data marts
source their information from the data warehouse. In the data warehouse,
information is stored in 3rd normal form.
Definition as Per Bill Inman:
“A data warehouse is a
subject-oriented, integrated, time-variant and non-volatile collection of data
in support of management's decision making process.”
Subject-Oriented: A data warehouse can be used to analyze a particular
subject area. For example, "sales" can be a particular subject.
Integrated: A data warehouse integrates data from multiple data
sources. For example, source A and source B may have different ways of
identifying a product, but in a data warehouse, there will be only a single way
of identifying a product.
Time-Variant: Historical data is kept in a data warehouse. For example,
one can retrieve data from 3 months, 6 months, 12 months, or even older data
from a data warehouse. This contrasts with a transactions system, where often
only the most recent data is kept. For example, a transaction system may hold
the most recent address of a customer, where a data warehouse can hold all
addresses associated with a customer.
Non-volatile: Once data is in the data warehouse, it will not change.
So, historical data in a data warehouse should never be altered.
His Approach towards the
Data warehouse Design is Top-Down.
In top-down approach the data warehouse design, in which the data
warehouse is designed using a normalized enterprise data model. "Atomic" data, that is, data at the
lowest level of detail, are stored in the data warehouse.
(It’s mean first
create Data warehouse and next Data marts)
This is the End-to End process of an application in the organization (From requirements gathering to production support).
ReplyDeleteThere are 4 phases in the life-cycle movers layton utah
It’s great to come across a blog every once in a while that isn’t the same out of date rehashed material. Fantastic read.
ReplyDeleteRPA Course Training in Chennai |Best RPA Training Institute in Chennai
AWS Course Training in Chennai |Best AWS Training Institute in Chennai
Devops Course Training in Chennai |Best Devops Training Institute in Chennai
Selenium Course Training in Chennai |Best Selenium Training Institute in Chennai
Java Course Training in Chennai | Best Java Training Institute in Chennai
Thanks to the author for this article. Data warehouses are becoming more and more necessary for conducting business and storing information. There are various options for such storage, but in my opinion, it is worth paying attention to the enterprise data warehouse architecture, it is the most structured solution.
ReplyDelete