Explain different Data Warehouse Models.

-

Data Warehouse Models: Enterprise Warehouse, Data Mart, and Virtual Warehouse

From the architecture point of view, there are three data warehouse models: the enterprise warehouse, the data mart, and the virtual warehouse.



a) Enterprise warehouse: An enterprise warehouse collects all of the information about subjects spanning the entire organization. It provides corporate-wide data integration, usually from one or more operational systems or external information providers, and is cross-functional in scope. It typically contains detailed data as well as summarized data and can range in size from a few gigabytes to hundreds of gigabytes, terabytes, or beyond. An enterprise data warehouse may be implemented on traditional mainframes, computer super servers, or parallel architecture platforms. It requires extensive business modeling and may take years to design and build.

b) Datamart: 

  • A data mart contains a subset of corporate-wide data that is of value to a specific group of users. The scope is confined to specific selected subjects. For example, a marketing data mart may confine its subjects to the customer, item, and sales. The data contained in data marts tend to be summarized.
  • Data marts are usually implemented on low-cost departmental servers that are Unix/Linux or Windows-based. The implementation cycle of a data mart is more likely to be measured in weeks rather than months or years. However, it may involve complex integration in the long run if its design and planning enterprise-wide. were not enterprise-wide.
  • Depending on the source of data, data marts can be categorized as independent or dependent. Independent data marts are sourced from data captured from one or more operational systems or external information providers, or from data generated locally within a particular department, or geographically sourced directly from enterprise data warehouses. area. Dependent data marts are sourced directly from enterprises' data warehouses.

c)Virtual warehouses: A virtual warehouse is a set of views over operational databases. For efficient query processing, only some of the possible summary views may be materialized. A virtual warehouse is easy to build but requires excess capacity on operational database servers.

                               OR,

From the perspective of data warehouse architecture, we have the following data warehouse models:

  • Virtual Warehouse
  • Datamart
  • Enterprise Warehouse

a)Virtual Warehouse

The view over an operational data warehouse is known as a virtual warehouse. It is easy to build a virtual warehouse. Building a virtual warehouse requires excess capacity on operational database servers.

b) Data Mart

Datamart contains a subset of organization-wide data. This subset of data is valuable to specific groups of an organization. In other words, we can claim that data marts contain data specific to a particular group. For example, the marketing data mart may contain data related to items, customers, and sales. Data marts are confined to subjects.

 c) Enterprise Warehouse

An enterprise warehouse collects all the information and the subjects spanning an entire organization. It provides us enterprise-wide data integration. The data is integrated from operational systems and external information providers. This information can vary from a few gigabytes to hundreds of gigabytes, terabytes or beyond.

Comments

Post a Comment

Popular posts from this blog

Suppose that a data warehouse for Big-University consists of the following four dimensions: student, course, semester, and instructor, and two measures count and avg_grade. When at the lowest conceptual level (e.g., for a given student, course, semester, and instructor combination), the avg_grade measure stores the actual course grade of the student. At higher conceptual levels, avg_grade stores the average grade for the given combination. a) Draw a snowflake schema diagram for the data warehouse. b) Starting with the base cuboid [student, course, semester, instructor], what specific OLAP operations (e.g., roll-up from semester to year) should one perform in order to list the average grade of CS courses for each BigUniversity student. c) If each dimension has five levels (including all), such as “student < major < status < university < all”, how many cuboids will this cube contain (including the base and apex cuboids)?

-
Image
a) A Snowflake Shema is shown in figure below: b) The specific OLAP operations to be performed: 1. Roll-up on course from course_id to department. 2. Roll-up on a student from student_id to university. 3. Dice on course, student with department ="CS" and university = "biguniversity" 4. Drill-down on a student from the university to student_name. c) N = 4 dimensions The cube will contain 54 = 625 cuboids.
Read more

Explain Parallel Efficiency of MapReduce.

-
Image
 PARALLEL EFFICIENCY OF MAP-REDUCE It is worth trying to figure out what is the parallel efficiency of MapReduce. Now let us assume that the data produced after the map phase is σ times the original data size D (σD), further, we assume that there are P processors which in turn perform map and reduce operations depending on which phase they are used in, so we do not have any wastage of processors. Also, the algorithm itself is assumed to do wD useful work, w is not necessarily a constant it could be D^2 or something like that, but the point is that there is some amount of useful work being done even if you have a single processor and that's wD. Now let us look at the overheads of doing the computation wD using MapReduce. After the map operation, instead of D data items, we have σD data items in P pieces, so each mapper writes data to their local disk. So, P there is some overhead associated with writing this data. Next, this data has to be read by each reducer before it can begin ...
Read more

Suppose that a data warehouse consists of the four dimensions; date, spectator, location, and game, and the two measures, count and charge, where charge is the fee that a spectator pays when watching a game on a given date. Spectators may be students, adults, or seniors, with each category having its own charge rate. a) Draw a star schema diagram for the data b) Starting with the base cuboid [date; spectator; location; game], what specific OLAP operations should perform in order to list the total charge paid by student spectators at GM Place in 2004?

-
Image
  b) The specific OLAP operations to be performed are: 1. Roll-up on date from date id to year. 2. Roll-up on spectator from spectator id to status. 3. Roll-up on location from location id to location name. 4. Roll-up on the game from game id to all. 5. Dice with status= “students”, location name= “GM Place”, and year=2004
Read more