Data WareHouse

ETL stands for Extract, Transform, Load is Data Warehouse acquisition processes that involves

• Extract the data from outside sources.

• Transform the data to fit business needs and ultimately

• Load the the transform data to the data warehouse.

For example:

1. Informatica.
2. Data Stage.
3. Oracle warehouse builder.
4. Ab initio.
   

ETL can also be used for the integration with legacy systems. ETL is the Data Warehouse acquisition processes of Extracting, Transforming and Loading data from source systems into the data warehouse.

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• View data from many different view point.

• Easily switch from one view point to another.

• Drill down in to the data with a parent-child relationship between the data point.

• Solve modern business problem such as market analysis and financial for casting.

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Query and reporting tools are divided in to two parts.

• Reporting tools
• Managed query tools

Reporting tools further dividing in to two parts.

• Production reporting tools will let companies generate regular operational reports or support high level batch job, such as calculating and printing paychecks.
• Report writer, on the other hand, are expensive desktop tools designed for end users.

Managed query tools protect end users from complexities of SQL and database structure by inserting a Meta layer between user and the database.

Meta layer is software that provides subject oriented view of database and support point-and-click creation of SQL.

These tools are designed for easy-to-use point–and-click and visual navigation operations that either accept SQL or generate SQL statements to query relational data stored warehouse.

Some of these tools are used to format the received data in to easy-to-read report.

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The principal purpose of data warehousing is to providing information to business users for strategic decision making.

These users interact with data warehouse using front-end tools. Although regular reports and custom reports are the primary delivery vehicles for analysis done in most data warehouse, many development efforts in data warehouse arena are focusing on exceptional reporting also known as alerts.

Example: If the data warehouse designed for accessing the risk for currency treading, an alert can be activated when a certain currency rate drops below a predefined threshold.

Access tools can be divided in to five main groups.

• Data query and reporting tools.
• Application development tools.
• Executive information system (EIS) tools.
• Data mining tools.

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Client:-

* GUI/Presentation logic
* Query specification
* Data Analysis
* Report formatting
* Data access

Application/Data Mart Server:-

* Summarizing
* Filtering
* Meta Data
* Multidimensional view
* Data access

DW Server:-

* Data logic
* Data services
* Meta data
* File services

Labels: Data WareHousing

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Client:-

* GUI/Presentation logic
* Query specification
* Data analysis
* Report formatting
* Summarizing
* Data access

DW Server:-

* Data logic
* Data services
* Meta data
* File services

Labels: Data WareHousing

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Multidimensional data model is to view it as a cube. The cable at the left contains detailed sales data by product, market and time. The cube on the right associates sales number (unit sold) with dimensions-product type, market and time with the unit variables organized as cell in an array.

This cube can be expended to include another array-price-which can be associates with all or only some dimensions.

As number of dimensions increases number of cubes cell increase exponentially.

Dimensions are hierarchical in nature i.e. time dimension may contain hierarchies for years, quarters, months, weak and day. GEOGRAPHY may contain country, state, city etc.





Data WareHousing

Labels: Data WareHousing

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Meta Data is one of the most important aspect of data warehousing. It is the data about data stored in data warehouse and its users.

Meta Data provides decision-support-oriented pointer to warehouse data and thus provide logical link between warehouse data and decision support application.

Meta Data is the key to providing user and application with a road map to the information stored in the warehouse.

Meta Data can define all attributes, data sources and timing, and rules that govern data use and data transformation of all data elements.

Labels: Data WareHousing

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There are mainly three type of Data Warehouse.................

1). Enterprise Data Warehouse.
2). Operational data store.
3). Data Mart.

Enterprise Data Warehouse provide a control Data Base for decision support through out the enterprise.

Operational data store has a broad enterprise under scope but unlike a real enterprise DW. Data is refreshed in rare real time and used for routine business activity.

Data Mart is a sub part of Data Warehouse. It support a particular reason or it is design for particular lines of business such as sells, marketing or finance, or in any organization documents of a particular department will be data mart

Labels: Data WareHousing

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1). Data Base (DB) is a place where data is taken as the base and manage to get a valuable for fast and efficient access.

Where as Data warehouse (DW) is the place where the application data is manage for analysis and reporting purpose.

2). Data Base is used for transactional purpose. It is volatile in nature i.e. value can change in DB.

Where as historical data is maintained in DW, instate of keeping it in real time OLTP (Data Base) system.

3). Data Base stores data in the form of table. Data is accessed by giving a query.

Data Warehouse is also a DB but the purpose is for business analysis and it is used to store historical data.

4). Transactional Data Base is RDB (relational database) with the normalize table.

Where as Data Warehouse contain complete data link past and present based on OLTP system with d normalize table.

Labels: Data WareHousing

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Data warehouse is a collection of historical data as well as current data.

Data warehouse is defined with the help of subject-oriented, time-variant, non-volatile and integrated data.

Subject-oriented

The data in the database is organized so that all the data elements relating to the same real-world event or object are linked together;

Time-variant

The changes to the data in the database are tracked and recorded so that reports can be produced showing changes over time;

Non-volatile

Data in the database is never over-written or deleted - once committed, the data is static, read-only, but retained for future reporting; and

Integrated

The database contains data from most or all of an organization's operational applications, and that this data is made consistent.

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