Data Warehouse : Database Analysis And Query

very important is the elimination of duplicate data, also known as redundant data, which not only

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

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 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.

1.     If I were to design Ben & Jerry’s data warehouse I would use several dimensions of information. The first dimension would consist of the company’s products; ice cream, frozen yogurt or merchandise. The marketing department has to know which products are selling, if Ben & Jerry’s didn’t know that their T-shirts are selling out as soon as they hit the stores, then they wouldn’t be able to take advantage of the opportunity to sell the shirts. The second dimension would consist of the different areas of sales; US, Canada, Mexico, or Europe. I am not sure if they sell their ice cream in Mexico, but with data collection they can find out if their ice cream would be a better seller in the hot climate,

Companies and organizations all over the world are blasting on the scene with data mining and data warehousing trying to keep an extreme competitive leg up on the competition. Always trying to improve the competiveness and the improvement of the business process is a key factor in expanding and strategically maintaining a higher standard for the most cost effective means in any business in today’s market. Every day these facilities store large amounts of data to improve increased revenue, reduction of cost, customer behavior patterns, and the predictions of possible future trends; say for seasonal reasons. Data

CHAPTER 2: DATA WAREHOUSING Objectives: After completing this chapter, you should be able to: 1. Understand the basic definitions and concepts of data warehouses 2. Understand data warehousing architectures 3. Describe the processes used in developing and managing data warehouses 4. Explain data warehousing operations 5. Explain the role of data warehouses in decision support 6. Explain data integration and the extraction, transformation, and load (ETL) processes 7. Describe real-time (active) data warehousing 8. Understand data warehouse administration and security issues CHAPTER OVERVIEW Data warehousing is at the foundation of most BI. This is the data warehousing chapter of the book. Later chapters will use it as they discuss DW

Extraction: Data is identified and extracted from one or more external different sources, including applications and database systems.

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:

·         Extracting data from source systems, transforming it, and then loading it into a data warehouse

Within an enterprise there are various different applications and data sources which have to be integrated together to enable Data Warehouse to provide strategic information to support decision-making. On-line transaction processing (OLTP) and data warehouses cannot coexist efficiently in the same database environment since the OLTP databases maintain current data in great detail whereas data warehouses deal with lightly aggregated and historical data. Extraction, Transformation, and Loading (ETL) processes are responsible for data integration from heterogeneous sources into multidimensional schemata which are optimized for data access that comes natural to human analyst. In an ETL process, first, the data are extracted from

Summary: The text book I have chosen is “The Data Warehouse Toolkit” third edition, written by Ralph Kimball and Margy Ross. This book mainly involves on techniques to develop the business in real-time. As the authors had a lot of experience because of their work from 1980’s, they have seen both the growth and failures of the companies in the market. Chapters in this text book involves goals of data warehousing which include Data staging area, data presentation, data access tools. Kimball modeling techniques involves gathering business requirements and data realities, business processes, different table techniques. Case studies in retail sales are explained in this text book, four step dimensional design process which includes the design process with the help of different dimensions and facts. In order management chapter it deals with the business processes that to be implemented in data warehouses as they supply core business performances metrics and finally provide the real time warehousing requirements. Customer relationship management involves in improving the customer relation with the company or product, understanding the needs of customer and providing high level service is the goal of this chapter. In accounting, we deal with model of general ledger information for the data warehouse, it describe the years and dates at which things to be happened and show different dimensional models which helps to combine the data from

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

The data in a data warehouse comes from operational systems of the organization as well as from other external sources. These are collectively referred to as source systems. The data extracted from source systems is stored in a area called data staging area, where the data is cleaned, transformed, combined, deduplicated to prepare the data for us in the data warehouse. The data staging area is generally a collection of machines where simple activities like sorting and sequential processing takes place. The data staging area does not provide any query or presentation services. As soon as a system provides query or presentation services, it is categorized as a presentation server. A presentation server is the target machine on which the data is loaded from the data staging area organized and stored for direct querying by end users, report writers and other applications. The three different kinds of systems that are required for a data warehouse are: