Data Warehouse: Data Normalization

Data Normalization splits up data to avoid redundancy (duplication) by moving commonly repeating groups of data into new tables. 

Database Normalization Example

The following graphic gives an example of a typical conversion of an un-normalized table to multiple normalized tables.

Advantages of Database Normalization:

• Data integrity is maintained.
• Results of queries are predictable.
• Changes to data have to be made only once.

Amount of storage required by the database is optimal.

Read more reference:
http://en.wikipedia.org/wiki/Data_normalization
http://en.wikipedia.org/wiki/Database_normalization
http://www.sqlsteps.com/what-sql-classes-dont-normally-cover
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Data Warehouse: Star Schema

Star Schema
In Data Warehouse, the combination of facts and dimensions is sometimes called a star schema.

The star schema (also called star-join schema, data cube, or multi-dimensional schema) is the simplest style of data warehouse schema. The star schema consists of one or more fact tables referencing any number of dimension tables.

Model (refer the graphic above)

• The fact table holds the metric values recorded for a specific event. Because of the desire to hold atomic level data, there generally are a very large number of records (billions). Special care is taken to minimize the number and size of attributes in order to constrain the overall table size and maintain performance. Fact tables generally come in 3 flavors - transaction (facts about a specific event e.g. Sale), snapshot (facts recorded at a point in time e.g. Account details at month end), and accumulating snapshot tables (e.g. month-to-date sales for a product).
  
• Dimension tables usually have few records compared to fact tables, but may have a very large number of attributes that describe the fact data.
  
  Read more: http://en.wikipedia.org/wiki/Star_schema

Data: Data Marts

Data warehouses can be subdivided into data marts. Data marts store subsets of data from a warehouse.

Data Mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse that is usually oriented to a specific business line or team.

In deployments, each department/business unit is considered the owner of its data mart including all the hardware, software and data.

Each department able to manipulate and develop their data any way they see fit; without altering information inside other data marts or the data warehouse. 

Read more: http://en.wikipedia.org/wiki/Data_mart

Data: Data Warehouse

I've been employed by new sub-govern company since last 2 weeks.
And till now I've been assigning different task from the first week to another weeks...
First, I was asked to backup finance system, then last week I was asked to help collection module for new system .. and today I just knowing will be assigned new task.. to handle data warehouse.
What is data warehouse?

Data Warehouse (DW or DWH) is a database used for reporting and data analysis. It is a central repository of data which is created by integrating data from multiple disparate sources. Data warehouses store current as well as historical data and are commonly used for creating trending reports for senior management reporting such as annual and quarterly comparisons.

The data may pass through an operational data store for additional operations before they are used in the DW for reporting.

ETL(extract, transform and load ) refers to a process in database usage and especially in data warehousing that involves:

• Extracting data from outside sources
• Transforming it to fit operational needs (which can include quality levels)
• Loading it into the end target (database, more specifically, operational data store, data mart or data warehouse)

The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database.

The integrated data are then moved to another database, often called the data warehouse database. 

Read more: http://en.wikipedia.org/wiki/Data_warehouse 

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