Explain Data Warehouse Features /Characteristics / Nature of Data Warehouse.

-

 Data Warehouse Features /Characteristics / Nature of Data Warehouse

The key features of a data warehouse are:

  • Subject-oriented: A data warehouse is organized around major subjects such as customer, supplier, product, and sales. Rather than concentrating on the day-to-day operations and transaction processing of an organization, a data warehouse focuses on the modeling and analysis of data for decision-makers. Hence, data warehouses typically provide a simple and concise view of particular subject issues by excluding data that are not useful in the decision support process.
  •  Integrated: A data warehouse is usually constructed by integrating multiple heterogeneous sources, such as relational databases, flat files, and online transaction records. Data cleaning and data integration techniques are applied to ensure consistency in naming conventions, encoding structures, attribute measures, and so on.
  • Time-variant: Data is stored to provide information from a historic perspective (e.g., the past 5-10 years). Every key structure in the data warehouse contains, either implicitly or explicitly, a time element.
  • Nonvolatile: A data warehouse is always a physically separate store of data transformed from the application data found in the operational environment. Due to this separation, a data warehouse does not require transaction processing, recovery, and concurrency control mechanisms. It usually requires only two operations in data accessing: initial loading of data and access of data.

                                             OR,

  • Subject Oriented: A data warehouse is subject-oriented because it provides information around a subject rather than the organization's ongoing operations. These subjects can be the product, customers, suppliers, sales, revenue, etc.
  • Integrated: A data warehouse is constructed by integrating data from heterogeneous sources such as relational databases, flat files, etc. Data cleaning and data integration techniques are applied to ensure consistency in naming conventions, encoding structures, attribute measures, and so on.
  •  Time-Variant: The data collected in a data warehouse is identified with a particular time period. The data in a data warehouse provides information from the historical perspective (e.g., the past 5–10 years).
  • Non-volatile: Non-volatile means the previous data is not erased when new data is added to it. A data warehouse is kept separate from the operational database and therefore frequent changes in the operational database are not reflected in the data warehouse.

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

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

Explain network topology .Explain tis types with its advantages and disadvantges.

-
Network topology Network topology refers to how the nodes and links in a network are arranged. A network node is a device that can send, receive, store, or forward data. A network link connects nodes and may be either cabled or wireless links. Understanding topology types provides the basis for building a successful network. There are a number of topologies but the most common are bus, ring, star, and mesh: A bus network topology is when every network node is directly connected to a main cable. In a ring topology, nodes are connected in a loop, so each device has exactly two neighbors. Adjacent pairs are connected directly; non-adjacent pairs are connected indirectly through multiple nodes. In a star network topology, all nodes are connected to a single, central hub and each node is indirectly connected through that hub.  A mesh topology is defined by overlapping connections between nodes. You can create a full mesh topology, where every node in the network is connected to every other
Read more