Traditional Enterprise Data Warehouse : Ebay Essay

An active data warehousing, or ADW, is a data warehouse implementation that supports near-time or near-real-time decision making. It is featured by event-driven actions that are triggered by a continuous stream of queries that are generated by people or applications regarding an organization or company against a broad, deep granular set of enterprise data. Continental uses active data warehousing to keep track of their company’s daily progress and performance. Continental’s management team holds an operations meeting every morning to discuss how their

Ebay is one of the world's largest e-commerce and multinational corporation. Here are some facts, the company was launched in 1995 as " Auction Web," on labor day weekend by Pierre Omidyar. Later the name was officially changed to ebay in 1997 because many customers and media coverage referred the website as ebay. It has over 200 million registered users by 2015 and branched out into twenty five countries. There are thirty five thousand employees working on eBay in that 42% are female. The company net revenue by 2014 is 17.9 billion dollars. There are about 25 million sellers and the number of items listed in the market places are 800 million. The daily search on eBay is 250 million and the hourly search is 11 million. 60% of company revenue comes from

Up until this point, Third Star Financial Services has operated via a succession of mergers and acquisitions where systems were inherited but never integrated into the network. Its data management has been virtually non-existent and entirely ineffective. Evidence of this can be found in the absence of an enterprise-wide data management solution and the presence of several disparate systems operating independently with no measurable benefit to the company. Due to a lack of actionable data, management makes decisions based on instinct rather than through analysis. A direct consequence of this is a steadily declining market share and loss of high-level employees to competing companies. Fortunately, this discrepancy has been identified and Third Star executives have established the new goal of modernizing and streamlining operations. Using concepts outlined by the Data Management Association (DAMA), this proposed enterprise architecture will allow Third Star to transform their data from a liability to an asset.

An enterprise data warehouse (EDW) makes information accessible to the applications utilized as a part of offices all through the association including engineering, human resources (HR), and strategic planning. Norfolk Southern assembled a TOP dashboard

Almost all internet based businesses today are designed and developed with the use of distributed object technology which requires security, high availability, high performance and scalability. It is also required to be able to handle very large amount of requests forwarded by its internet community. It must also be capable of responding to all of these requests adequately and in very timely fashion. It’s not possible for any customer to be able to go through lacs of products available on its site (WeForum, 2015). The main requirement for it grow, was that they be able to make sense and figure out the huge amount of data generated for its sellers so that they can position all their products in such a way that right kind of product is displayed to the right customer. What ebay.com needed was a product or service which could be able to handle thousands of petabytes data generated by customer’s activity on the site. And, this product or service should be

What information is accessible? The data warehouse offers possibilities to define what’s offered through metadata, published information, and parameterized analytic applications. Is the data of high value? Data warehouse patrons assume reliability and value. The presentation area’s data must be correctly organized and harmless to consume. In terms of design, the presentation area would be planned for the luxury of its consumers. It must be planned based on the preferences articulated by the data warehouse diners, not the staging supervisors. Service is also serious in the data warehouse. Data must be transported, as ordered, promptly in a technique that is pleasing to the business handler or reporting/delivery application designer. Lastly, cost is a feature for the data

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.

Promote and lead: Identifying the administrative and cultural barriers such as big data leadership, data cycle,

A data warehouse and business intelligence application was created as part of the Orion Sword Group project providing business intelligence to order and supply chain management to users. I worked as part of a group of four students to implement a solution. This report reflects on the process undertaken to design and implement the solution as well as my experience and positive learning outcome.

Therefore, eBay focuses on a large volume of produced output. In addition, it focuses on products which can effectively respond to the huge customers, better speed, stability and security of its website.

This report examines the emergence of U-commerce and the implications on data management it’s faced with. Through research of real cases, the paper will examine how U-commerce has been implemented into the operations of businesses and the roles that it plays. It will also provide basic examples of the four elements which make up U-commerce, Ubiquitous, Universal, Unique, and Unison. The paper will address the importance and growing concern of data management of this technology. Enterprise data has never been more accessible to users and across devices than it is now. Assuring the right data makes it to the right places and people, can be very critical to a business’s operations or decision strategies. With a multitude of devices with various interfaces, U-commerce’s data management stability, and privacy is continuously at risk and monitored. The paper will provide a sound rational for why today’s businesses need to make sure that data management is a top priority, as they move into new phases of outlets for doing business, and share business related information.