shortly, 
1. write the modified data to the archive redo log file and 2. update control file to indicate the most recent checkpoint 


in detail 
1. The data set for the update is taken from the DB CACHE.

2. The ‘Pre Change’ data is written to the UNDO CACHE.

3. The ‘Post Update’ data is rewritten to the REDO CACHE as a delta (a piece of data that contains enough information to reconstruct the original data). The ‘Post Update’ data from the UNDO CACHE is also written to the REDO CACHE (alternatively known as the REDO LOG BUFFER).

4. After a COMMIT is issued the LOG WRITER Process (LGWR) writes the REDO CACHE to the REDO LOG FILES.

5. If ARCHIVELOG is enabled then the ARCHIVER Process (ARCH) writes a copy of the REDO LOG FILES to an alternative destination(s) (Up to 10) before the REDO LOGS are reused.

6. The CHECKPOINT Process (CKPT) is called regularly and this ensures that changed data (dirty blocks) are written from the DB CACHE and back to the datafiles. In addition this process updates the CONTROL FILES.

alter database backup controlfile to ['filename' | trace]

alter database backup controlfile to '/some/arbitrary/path';
alter database backup controlfile to '/some/arbitrary/path' reuse;

alter database backup controlfile to trace;
alter database backup controlfile to trace as '/some/arbitrary/path';
alter database backup controlfile to trace as '/some/arbitrary/path' reuse;

• Requirement Gathering
• Physical Environment Setup
• Data Modeling
• Task Description
• the logical data model is built based on user requirements, and then it is translated into the physical data model. The detailed steps can be found in the Conceptual, Logical, and Physical Data Modeling section.
• Time Requirement  
•   2 - 6 weeks.
• 
  Deliverables
  
  • Identification of data sources.
  • Logical data model.
  • Physical data model.

• ETL
•   Task Description
• The ETL (Extraction, Transformation, Loading) process typically takes the longest to develop, and this can easily take up to 50% of the data warehouse implementation cycle or longer. The reason for this is that it takes time to get the source data, understand the necessary columns, understand the business rules, and understand the logical and physical data models.
• Time Requirement
• 1 - 6 weeks.
• Deliverables
• Data Mapping Document
• ETL Script / ETL Package in the ETL tool

• OLAP Cube Design
•   Task Description
• Usually the design of the olap cube can be derived from theRequirement Gathering phase.Usually  the design of the olap cube can be derived from theRequirement Gathering phase.
• Time Requirement
  
  

  1 - 2 weeks.

  
  Deliverables
• Documentation specifying the OLAP cube dimensions and measures.
• Actual OLAP cube / report.

• Front End Development
•   Task DescriptionRegardless of the strength of the OLAP engine and the integrity of the data, if the users cannot visualize the reports, the data warehouse brings zero value to them. Hence front end development is an important part of a data warehousing initiative. he most important thing is that the reports should need to be delivered over the web, so the only thing that the user needs is the standard browser.
• Time Requirement
  1 - 4 weeks.
  Deliverables
  Front End Deployment Documentation
• Report Development
•   Task Description
• Report specification typically comes directly from the requirements phase. To the end user, the only direct touchpoint he or she has with the data warehousing system is the reports they see. So, report development, although not as time consuming as some of the other steps such as ETL and data modeling, nevertheless plays a very important role in determining the success of the data warehousing project.
•   Time Requirement1 - 2 weeks.
  Deliverables
• Report Specification Documentation.
• Reports set up in the front end / reports delivered to user's preferred channel.

• Performance Tuning
• Query Optimization
• Quality Assurance
• Rolling out to Production
• Production Maintenance
• Incremental Enhancements

Extract, transform, load

A data warehouse is a place where data is stored for archival, analysis and security purposes. Usually a data warehouse is either a single computer or many computers (servers) tied together to create one giant computer system.

Besides being a store house for large amount of data, they must possess systems in place that make it easy to access the data and use it in day to day operations. A data warehouse is sometimes said to be a major role player in a decision support system (  DSS). DSS is a technique used by organizations to come up with facts, trends or relationships that can help them make effective decisions or create effective strategies to accomplish their organizational goals.

Data Warehousing Models

Types of Data Warehouses