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 …show more content…
A repository of current and historical data of potential interest to managers throughout an organization. Data are usually structured so that they are ready for analytical processing (for OLAP, data mining, reporting, querying, etc.) DW provides a single version of the truth 4. (Note: Watson, 2005, refers to the term data warehousing as a discipline resulting from applications that provide decision and support capabilities) C. Characteristics of data warehousing (Inmon, 2005) to ensure that the DW is tuned almost exclusively for data access: 1. Subject Oriented – data are organized by topics, such as sales, products, customers, etc. Best for providing a more comprehensive view of the organization; not only how a business is operating, but why. Integrated – data from different sources are stored in a consistent format. Also clarity is obtained in unit of measures, naming/labeling of attributes, etc. (The assumption is the data warehouse is totally integrated.) Time Variant – provides data at various points in time (daily, weekly, monthly quarterly, annually – historic and current data so as to analyze trends, deviations, compare and forecast outcomes, etc.). Every data warehouse should have a time variable. (Example: LSU enrollment, retention, graduation data) Nonvolatile – users cannot change the data once entered into the data warehouse. This ensures that the data warehouse is almost exclusively
Real-time data warehousing creates some special issues that need to be solved by data warehouse management. These can create issues because of the extensive technicality that is involved for not only planning the system, but also managing problems as they arise. Two aspects of the BI system that need to be organized in order to elude any technical problems are: the architecture design and query workload balancing.
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
Q3: While this case study supports a specific data warehouse product, please locate another case study from another data warehousing Software Company and explain the data warehouse that was designed in that case study?
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
The data warehousing system will also allow the company to use a data model and server technology that speeds up querying and reporting. This is because these will not be included in the data processing time thus allowing the company to use a modeling technique that does not slow down or complicate the transaction processing system. The data warehouse will also allow the company to use a bit-mapped indexing system as their server technology in order to speed up query and report processing. Technologies for transaction recovery will also be employed to speed up transaction
Google is able to use data warehousing to improve its business. A data warehouse is a logical collection of data that supports business analysis activities and decision making tasks. Google can use a data warehouse to store information just like a database is able to, but in an aggregated form more suited to supporting decision-making tasks.
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,
Data Warehouses and Data Marts: A Dynamic View By Joseph M. Firestone, Ph.D. White Paper No. Three March 27, 1997
Data warehouse – focuses primarily on storing data used to generate information required to make tactical or strategic decision. (pg. 9)
This paper will present the return on investment (ROI) of data warehousing (DW). The history of data warehousing is based on the definition and timeline. Then, detailed information about return on investment will be discussed. Following, will be information about data warehousing new technology of hardware and software. Data Warehousing is a new term in my department where we use the Network Appliance (NetApps) Netfiler storage devices/units. The information read was very informative and helpful in my understanding data warehousing better. Finally, a conclusion about the return on investment of data warehousing.
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
Data warehouse helps in solving and managing the data from various sources and transactional systems with more speedy and efficiently, and converts those data into practical information. Along with, data warehouse serves in processing of large and complex queries in a highly-efficient manner.
A common feature of data warehouse on which most of the scholars agree upon is that a data warehouse acts as storage of historical data. This
First 3 chapters of the book are written in a way that beginners may get clear view of the basic concepts. First chapter described the need regarding strategic information, information crisis, and that the data warehousing is a better solution for information crisis. Features and components of Data warehouse, along with the concept and need of metadata is described. Various trends in data warehouse are mentioned by the author based on his own industrial experience. Areas like Continued growth in data warehousing