Miller Inc. is a company that wishes to develop a new and more efficicent data repository for all data collected, stored, and transferred. Their desire to create a data warehouse that operates quickly with less effort is the purpose of this project. Adaptation of database modeling along with designing their data warehouse will lead to higher consumer and employee satisfaction. The project goal is to create a database schema to work as well be designed alongside other components such as identifying metadata in order to let IT model the data warehouse, implement and test it. to identify and gather database requirements, design the dimensional model, develop the system architecture, design the relational database and online transactional processing model, develop the data maintenance application, develop analysis applications to test and deploy the system through a series of steps intended to reduce error rate. The types of applications for use will be loading, indexing and summarizing tables, scanning, joining, sorting, aggregating and fetching data. These kinds of applications will be most effective in dealing with the company's data-intensive tasks.

Sites will also be used along with the applications in order to perform live testing. One of the objectives is to test in real time. Consumers will use the sites as well after initial testing. Maintenance as well as feedback will be done periodically to maintain smooth data transactions. One of the most important sites will be the one used for data backups. This is to help keep errors and crashes to a minimum.

Deliverable 2 discusses the customizations as well as the interfaces Miller Inc. could incoorperate into the design of a new database warehouse. Limitations of the customizations as described in the deliverable, are set by calculated data values. These set values help decrease the amount of in the system as well as ensuring the system is scalable to include other forms of data for future analysis and use. The interfaces are customer, analysts and administrator interfaces. All of the interfaces will be accessible through web-based applications allowing consumers/customers to browse anonymously. The adminstrator will have access to the site and interfaces as well.

Application and technical architecture will be three-layer architecture comprised of the source, reconciled and, data warehouse layer. The ETL process will highlight the overall structure needed to carryout the processes. Deliverable 2 also discusses the need for the company to put implace a transaction processing system due to the high levels of data processing beyond the normal capacity for performing server and disk bound tasks which are slowed down through querying and reporting. A need to eliminate servers and disks not used for processing must be made clear.

A data warehouse will provide the fastest way to process transactions in a time frame that is manageable. Resources, space, and time are valuable and with a data warehouse, it also allows for less server and disk resources used, minimizing transaction processing time. This in turn, helps give Miller Inc. The much needed boost in performance and speed. Also, the separate servers utilized by the data warehouse will allow for faster querying and reporting as well. Product offering due in part to the speediness of the new system will allow Miller Inc. To give consumers the ability to customize their reports greatly and also presenting them with more options as discussed in deliverable 3.

Of the many benefits of designing a data warehouse, the change to also provide a repository in which the company can use to clean up its transaction processing system also allows for smoother queries and reports. Data warehousing allows for multiple source data handling. It allows for faster reaction time as well as transaction time. And lastly, it allows for efficient data storage.

Data archival is increased with data warehousing. It does this by providing a more stable system than traditional relational databases. This is because new data fields and metadata can updated, modified, and added quickly and smoothly over time, at any time. This allows information to be relayed to consumers without sacrificing performance or speed. Data warehousing increases the consistency and accuracy of report scheduling enabling users to access better reports. Data warehouses make it easy to use the interfaces, exchange data, and promote higher consumer satisfaction.

The star database schema was elected because it provides a fact table acting as a reference for primary keys and foreign keys. How it works is the fact table references primary keys in the various dimension tables. The fact table is useful for storing any and all data entered. It stores records that consist of numeric values and foreign keys that lead to dimensional data where further descriptive information is stored. The fact table for this data warehousing project...

It achieves this through current collection of data. These are then related to other entities such as: visitors, visits, sessions, and user data. Everything is then linked in the fact table described in the database schema previously. The chosen entity relationship model allows for data processing through the OLTP system making it highly structured, allowing twice the transactions.
Deliverable 4 disccusses the use of analytics and cloud technology giving the system an extra boost in processing speed. It would allow companies like Miller Inc. To take full advantage of the large amount of data collected through web analytics services. Data dynamic in nature is difficult to process. Cloud technology generates the processing strength needed to create avenues for better data management and visualization. Cloud computing also uses a system architecture consisting of clusters of low-cost, largely distributed servers .

These are designed to work with a server virtualization layer, data storage and parallel programming library. Several huge advantages comes from this cloud computing architecture like cost savings. Cost savings grants Miller Inc. less need for resources to be spent on developing, implementing and maintaining a cloud computing analytics service. Secondly, the system can be expanded allowing the company future growth. High reliablility and stability is also a big advantage. I will be able to handle frequent system failures. Lastly, is the efficiency that cloud computing will add to Miller Inc.'s analytics system through less use of computer, network and disk resources.

Deliverable 5 discusses security and maintenance of security system. Miller Inc. operates a wide multitude of functions, information systems and physical assets in its cloud architecture. These integrate with the critical cloud infrastructure delivering various and efficient services to the consumers. This also presents th company with a need to improve security. Network security and information security must be met with deep contemplation and considertion when designing and deploying its network. Stopping malicious packets from entering and leaving the network should be first priority in order to avoid costly and timely delays.

Focus on security will also ensure unanticipated breaches will be dealt with, without essential information leaving the network. Security also involves deploying essential tools that will ensure the detection of any attempts to intrude the network. Detection of intelligent network sniffers through use of highly customized security policies assist in generating logs, centralized logging that prevent network overload. Security will be achieved through the use of several central firewalls and routers easily configurable to detect any changes in network usage. These will be analyzed using proprietary security monitoring software to ensure that the system is highly protected.

To enable the company to determine the real world data warehouse requirements into a cost comparison model/analysis, one has to look at how other vendors price and describe their features needed by actual customers at an advanced level. From these generic high level requirements a preliminary determination about what vendor-specific options must be bought to support the generic needs can be identified. The generic, high-level requirements as defined follows:

• Database -- relational database software used to store the raw data generating the base for the data warehouse

• Clustering -- software feature used with the Database to create larger data warehouse by splitting the data between multiple instances of the database.

• Partitioning -- software feature that allows the data from a single table to be split into chunks within the data warehouse such that it is easier to manage and to use

(required for scaling of data)

• ETL -- Extract, Transformation, and Load software used to extract data from operational (e.g., OLTP) and other source systems. The software also needs to be able to transform the data into a suitable format and encoding and then load it into the data warehouse.

• Query Manager -- software used to keep one user from using all of the resources of the data warehouse.

• OLAP -- Online Analytical Processing, software used to create a multidimensional view of the data for quick and efficient access to aggregated or summary data.

• Data Mining -- software used to find patterns and aberrations in the data that might lead to useful business insight.

• Data Visualization -- software used to display patterns and aberrations in…