Unit-1.pptx final unit new mtech unit thre
- 1. Unit-1 Submitted by Javed Akhtar M.Tech part time(CSE)
- 2. • In computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a database used for reporting (1) and data analysis (2). • Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW).
- 3. • The typical extract transform load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. • The staging layer or staging database stores raw data extracted from each of the disparate source data systems. • The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database.
- 4. • The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups often called dimensions and into facts and aggregate facts. • The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.
- 6. • The integrated source data systems and the data warehouse are all integrated since there is no transformation of dimensional or reference data. • This integrated data warehouse architecture supports the drill down from the aggregate data of the data warehouse to the transactional data of the integrated source data systems. • A data mart is a small data warehouse focused on a specific area of interest. Data warehouses can be subdivided into data marts for improved performance and ease of use within that area.
- 7. 1. Keep things focused. • "Try not to create a global solution." Kramer suggests that a good practice is to "focus on what you need. A small data warehouse or data mart which addresses a single subject or that is focused on a single department is much more efficient than a large data warehouse. • You will see measurable results much faster from a data mart than a data warehouse. A focused data mart will get funding and gain organizational consensus a lot easier, too." 2. Don't worry about integration, keep things small. • "Integration can be an issue, but it has always been a problem when organizations try to take a small filing system and integrate it into an organizational system. There are always
- 8. 3. Spend the extra money if you need help designing your system. Kramer commented, "Systems designing is the best place to spend the money on hiring consultants. They know the problems, and know how to deal with them. It is possible to design your own data warehouse system, but it is a lot less frustrating to hire out the design process."
- 9. 4.minimizes, or possibly eliminates any tool integration issues," Kramer advised. 5. Be in tune with the users. • "Know your users," Kramer warned. "If you are not careful, you will wind up giving the right users the wrong tools, and that only leads one place - frustration. • Find out who your end-users are, and work backward to the operational data. This will tell you what tools your data warehouse needs."
- 10. • 6. Consider your platforms. • Kramer said "there really are no right platforms out there. You can start with a UNIX system or NT. Keep in mind that the NT has a ceiling in terms of scalability, but it works well with data marts, and most other small warehouses, just not global data warehouses." • 7. Think before you data mine. "Data mining is a solution in search of a problem," Kramer said. "Know what you want to find before you select the tool. Data mining software simply relieves some of the burden from the analyst."