The Development Of Data Warehouse Essay

In this figure, there are two sources of information for business: 1). Information from IW, which has passed through the ETL process before it goes to the end users. 2.) Information from independent data sources, which is processed by different standards. This is counter to the strategic goal of eliminating duplication of analysis, reporting and ad hoc analysis. This environment consumes more time for business users to understand end-user data, and more money for the firm which is wasted on different and possibly conflicting results.

Data warehouse has different concepts of data. Each concept is divided into a specific data mart. Data mart deals with specific concept of data, data mart is considered as a subset of data warehouse. In Indiana University traditional data warehouse is unable to create large data storage. Further it shows any errors and imposed rules on data. The early binding method is disadvantage. It process longer time to get enterprise data warehouse (EDW) to initiate and running. We need to design our total EDW, from every business rule through outset. The late binding architecture is most flexible to bind data to business rules in data modeling through processing. Health catalyst late binding is flexible and raw data is available in data warehouse. It process result by 90 days and stores IU data without any errors.

The Enterprise Data Warehouse is the primary data storage for USPS. It approximately 35 petabytes of storage capacity which allows it to store all the data collected from over 100 systems ranging from financial, human resources, transactional, etc. To process and store data into the EDW, it requires three steps of extract, transform and load. During the extraction process, the data is taken from the source of different systems within the USPS facilities. Then the transform process structures the data using rules or tables and turns it into one consolidated warehouse format. It also combines some data with others so it is easier to be transferred between different databases. The final process is the load with is basically integrating and writing the data into the database which can be accessed from any facilities and systems within the USPS. The EDW allows USPS to store any amount of data as efficient as possible at the lowest cost and quickest processing speed. It also allows the data to be used and migrate from database to database easily for analysis.

Extraction, Transformation, and Loading processes are responsible for the operations taking place in the back stage of a data warehouse architecture. In a broader aspect, initially the data is extracted from the source data stores which could be On-Line Transaction Processing or Legacy system, files of any formats, web pages or any other documents like spreadsheets or text documents. In this step, only the data which is different from the previous execution of ETL process (newly inserted, updated) gets extracted from the sources. Next, the extracted data is sent to Data Staging Area where the data is transformed and cleaned. Finally, the data is loaded to the central data warehouse and all its counterparts e.g., data marts and views. (Kabiri & Chiadmi 2013, p.1)

A data warehousing is defined as a collection of data designed to support management decision making. Data warehouses contains a wide variety of data that present a coherent picture of the business conditions at a single point in time. Development of a data warehouse includes development of the systems that extract data from operating systems plus the installation of the warehouse database system that provides managers flexible access to the data. The term data warehousing generally refer to the combination of many different databases across an entire enterprise. (webopidia)

A data warehouse is a large databased organized for reporting. It preserves history, integrates data from multiple sources, and is typically not updated in real time. The key components of data warehousing is the ability to access data of the operational systems, data staging area, data presentation area, and data access tools (HIMSS, 2009). The goal of the data warehouse platform is to improve the decision-making for clinical, financial, and operational purposes.

The state of affairs in the field of data warehousing and offers a variety of approaches to

Author will explore from Microsoft, Informatica and Oracle websites for more enhancements of the features. Spending quality time for this session will help author to make flawless comparison on ETL tool.

Chapter 11 Enterprise Resource Planning Systems 1. Closed database architecture is a. a control technique intended to prevent unauthorized access from trading partners. b. a limitation inherent in traditional information systems that prevents data sharing. c. a data warehouse control that prevents unclean data from entering the warehouse. d. a technique used to restrict access to data marts. e. a database structure that many of the leading ERPs use to support OLTP applications. 2. Each of the following is a necessary element for the successful warehousing of data EXCEPT a. cleansing extracted data. b. transforming data. c. modeling data. d. loading data. e. all of the above are necessary. 3. Which of the following is typically NOT part of

Enterprise Data Warehouses (EDW) have become the foundation of many enterprises' systems of record, serving as the catalyst of strategic initiatives encompassing Customer Relationship Management (CRM), Supply Chain Management SCM) and the pervasive adoption of analytics and Business Intelligence (BI) throughout enterprises. The role of databases continues to be an ancillary one, supporting the overall structural and data integrity of the EDW and increasing its value to the overall enterprise (Phillips, 1997). The advances made over the last decade in the areas of Extra, Transact & Load (ETL) have made it possible to create EDW frameworks and platforms more efficiently, creating greater accuracy in overall database and data warehouse performance as a result (Ballou, Tayi, 1999). The creation and use of an EDW to further drive an organization to its objectives requires that the differences between databases and data warehouses be defined, in addition to a clear, concise definition of just what data warehouse technologies are. Finally, the relationship between data warehouses and business intelligence (BI) including analytics needs analysis and validation. Each of these three areas are discussed in this analysis.

Many organizations want to implement an integrated enterprise warehouse that collects information about all subjects (e.g., customers, products, sales, assets, personnel) spanning the whole organization. However, building an enterprise warehouse is a long and complex process, requiring extensive business modeling, and may take many years to succeed. Some

Always access and process the data you need. Improve data integrity at the source with automatic processes that consolidate, cleanse and standardize your data directly in your operational environments. Offer a collaborative environment with a common set of tools that promote the reuse and sharing of data to achieve faster results and lower costs. Deliver consistent, trusted and verifiable

Data Transformation are often very complex and is the most costly section of the ETL process. Transformations are often achieved outside the database using flat files, but mostly occurs within an Oracle database. The transform step applies rules or functions to the extracted data. These rules or functions will decide on the analysis of data and can involve transformations like the following:

With respect to the web warehouse, schema integration is one of its important perspectives. There are heterogeneous sources of data in World Wide Web which vary in their schema configuration. Schemas from different

Data warehouse are multiple databases that work together. In other words, data warehouse integrates data from other databases. This will provide a better understanding to the data. Its primary goal is not to just store data, but to enhance the business, in this case, higher education institute, a means to make decisions that can influence their success. This is accomplished, by the data warehouse providing architecture and tools which organizes and understands the