business metadata

October 15, 2015 learndmdwbi Leave a comment

Metadata:

Metadata is data about data. Metadata comes into picture when we need to know about how data is stored and where it is stored. Metadata tools are helpful in capturing business metadata and the following section explains business metadata.

Business Metadata:

In IT, Business Metadata is adding additional text or statement around a particular word that adds value to data. Business Metadata is about creating definitions, business rules. For example, when tables and columns are created the following business metadata would be more useful for generating reports to functional and technical team.

The advantage is of this business metadata is whether they are technical or non-technical, everybody would understand what is going on within the organization.

Table’s Metadata: While creating a table, metadata for definition of a table, source system name, source entity names, business rules to transform the source table, and the usage of the table in reports should be added in order to make them available for taking metadata reports.

Column’s Metadata: Similarly for columns, source column name (mapping), business rules to transform the source column name, and the usage of the column in reports should be added for taking metadata reports.

Business Metadata Example:

Relational Databases

September 1, 2015 learndmdwbi Leave a comment

Database – RDBMS:

There are a number of relational databases to store data. A relational database contains normalized data stored in tables. Tables contain records and columns. RDBMS makes it easy to work with individual records. Each row contains a unique instance of data for the categories defined by the columns.

RDBMS are used in OLTP applications(e.g. ATM cards) very frequently and sometimes data warehouse may also use relational databases. Please refer to Relational data modeling for details to know how data from a source system is normalized and stored in RDBMS databases.

Popular RDBMS Databases:

RDBMS Name	Company Name
Oracle	Oracle Corporation
IBM DB2 UDB	IBM Corporation
IBM Informix	IBM Corporation
Microsoft SQL Server	Microsoft
Sybase	Sybase Corporation
Terradata	Teradata Corporation

DW & ETL

Data Warehouse Concepts

September 1, 2015 learndmdwbi One comment

What is a Data Warehouse?

According to Inmon, famous author for several data warehouse books, “A data warehouse is a subject oriented, integrated, time variant, non volatile collection of data in support of management’s decision making process”.

In order to store data, over the years, many application designers in each branch have made their individual decisions as to how an application and database should be built. So source systems will be different in naming conventions, variable measurements, encoding structures, and physical attributes of data. Consider a bank that has got several branches in several countries, has millions of customers and the lines of business of the enterprise are savings, and loans. The following example explains how the data is integrated from source systems to target systems.

Example of Source Data:

System Name	Attribute Name	Column Name	Datatype	Values
Source System 1	Customer Application Date	CUSTOMER_APPLICATION_DATE	NUMERIC(8,0)	11012005
Source System 2	Customer Application Date	CUST_APPLICATION_DATE	DATE	11012005
Source System 3	Application Date	APPLICATION_DATE	DATE	01NOV2005

In the aforementioned example, attribute name, column name, datatype and values
are entirely different from one source system to another. This inconsistency in data can
be avoided by integrating the data into a data warehouse with good standards.

Example of Target Data (Data Warehouse):

Target System	Attribute Name	Column Name	Datatype	Values
Record #1	Customer Application Date	CUSTOMER_APPLICATION_DATE	DATE	01112005
Record #2	Customer Application Date	CUSTOMER_APPLICATION_DATE	DATE	01112005
Record #3	Customer Application Date	CUSTOMER_APPLICATION_DATE	DATE	01112005

In the above example of target data, attribute names, column names, and datatypes
are consistent throughout the target system. This is how data from various source system
is integrated and accurately stored into the data warehouse.

Data Warehouse Architecture Diagram:

DW & ETL