Degenerated Dimensions

Degenerated Dimension:-

The columns in the fact that are participated in some sort of analysis but do not have any relation with any of the existing dimension, they are kept in the degenerate dimension and used as and when needed, while during ETL process remember that normally the degenerate dimension has the  same cardinality as of fact table.

Data warehousing Approaches

Data Warehousing:-

A centralized data repository designed with enterprise-wide usage in mind. Data warehouse provides facility for getting quick, accurate, and often insightful information. A Data Warehouse is designed so that its users can recognize the information they want and access that information using simple tools. A Data Warehouse integrates operational data from various sources into a single and consistent architecture that supports analysis and decision-making within the organization.

Data warehouses may consist of large amount of data, sometimes in smaller logical units called Data marts. Often the schemas of data marts are stored in what are known as “star schemas”, or dimensional modeling form; however there is no industry standard requiring that the schemas of data marts be in any particular form. Data warehouses are usually accessed (queried) via “data marts”, which are purpose-specific access points to or sub-sets of the warehouse. Data marts are designed to answer the probable queries of a given kind of user.

Data warehousing Approaches:-

With Data Warehousing we have two schools of thought.

Bill Inmon’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.

Inmon beliefs in creating a data warehouse on a subject-by-subject area basis. Hence the development of the data warehouse can start with data from the online store. Other subject areas can be added to the data warehouse as their needs arise. Point-of-sale (POS) data can be added later if management decides it is necessary.

Ralph Kimball Methodology-

Kimball views data warehousing as a constituency of data marts. Data marts are focused on delivering business objectives for departments in the organization. And the data warehouse is a conformed dimension of the data marts. Hence a unified view of the enterprise can be obtain from the dimension modeling on a local departmental level.

What is Business Intelligence

Business Intelligence

Business Intelligence

Business Intelligence is about getting the right information, to the right decision makers, at the right time. To understand first glimpse of BI, kindly read the following definitions of Business Intelligence.
  • Business Intelligence, or simply, “BI,” is the act of capturing raw data, then transforming and combining that data into information that can be proactively used to improve business.  The goal of BI is to empower decision-makers, allowing them to make better and faster decisions.  Better decisions make better business!
BI leads to:
  • fact-based decision making
  • “single version of the truth”

Business Intelligence (BI) is a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions.

BI applications include the activities of decision support, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.

Here I would like you guys to listen and watch this video on a very brief introduction to Business Intelligence

OLAP Terminology- About OLAP

OLAP and Data warehouse made easy

Developers of OLAP and multidimensional databases speak their own slang and use terms such as cubes, dimensions, measures, and members. Allow me to define these terms with a real life example that illustrates these terms more easily. Read full story »

Slowly Changing Dimension (SCD) Types

SCD Types

In his book The Data Warehouse Toolkit, Ralph Kimball brought us the idea of slowly changing dimensions. In fact he defined three types of slowly changing dimensions – called Type 1, Type 2, and Type 3.

Type 1
The Type 1 slowly changing dimension is simply overwriting the row in the dimension with the new data. There is no history kept. Doesn’t that defeat the purpose of a subject-oriented, time variant, integrated,non-volatile data store? Yes, it does, but it is also the easiest type to implement and uses the least amount of storage. After all, we areused to reading rows and updating them – that is what transaction systems are all about. Read full story »

Change Data Capture (CDC) Techniques

Change data capture

How do you determine what has changed from the source system in order tomove the data to the data warehouse or, in some cases, from the datawarehouse to the data mart?

Basically, there are four primary techniques to capture data from thesource systems -
1)Sequential processing
2)Date and time stamps
3)Triggers
4)The coined phrase “change data capture”

Read full story »

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