DATA WAREHOUSE

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A data warehouse is a database used to store data. It is a central repository of data in which data from various sources is stored. The purpose of a data warehouse is to provide flexible access to the data to the user. Data warehousing generally refers to the combination of many different databases across an entire enterprise. Data warehouses store current as well as historical data, so that all of the relevant data may be used for analysis. The analysis helps to find and show relationships among the data, to extract meaning from the data.

A database, on the other hand, is the basis or any data storage. It is an organized collection of data. Data from various sources are collected in to a single place, this place is the database.

A database and a data warehouse may seem the same, they are actually different is a key aspect. A database is used to store data while a data warehouse is mostly used to facilitate reporting and analysis. Basically, database is just where the data is stored; in order to access this data or analyze it a database management system is required. However, a data warehouse does not necessarily require a DBMS. The purpose of a data warehouse is for easy access to the data for a user. The data warehouse may also be used to analyze the data; however the actual process of analysis is called data mining.



EXTRACTION, TRANSFORMATION and LODING (ETL) PROCESS

ETL is a process in data warehousing responsible for pulling data out of the source systems and placing it into a data warehouse. ETL involves the following tasks :



ONLINE ANALYTICAL PROCESSING (OLAP)

OLAP (Online Analytical Processing) is computer processing that enables a user to easily and selectively extract and view data from different points of view. OLAP allows users to analyze database information from multiple database system at one time. OLAP data is stored in multidimensional databases. There are four type of OLAP servers :

  1. Relational OLAP (ROLAP)
  2. Multidimensional OLAP (MOLAP)
  3. Hybrid OLAP (HOLAP)
  4. Specialized SQL Servers



DATA MARTS

A data warehouse (DWH) is a system used to store information for use in data analysis and reporting. Data marts are areas of a data warehouses used to store information needed by a single department or even by an individual user. (Think of the DWH as a building, and data marts as offices inside the building.)

Schema is a logical description of the entire database. It includes the name and description of records of all record types including all associated data-items and aggregates. Much like a database, a data warehouse also requires to maintain a schema. A database uses relational model, while a data warehouse uses Star, Snowflake, and Fact Constellation schema.



DIMENSION TABLE

Dimension tables are used to describe the data we want to store. For example: a retailer might want to store the branch and product involved in a specific sale. Each dimension table is its own category (branch, product) and can have one or more attributes. For each branch, we can save its location at the street, city, state and country level. For each product, we can store the product id, product name, unit price etc. This is related to the hierarchy of attributes in the dimension table.




STAR SCHEMA

NOTE − Each dimension has only one dimension table and each table holds a set of attributes. For example, the Branch dimension table contains the attribute set {Branch_id, Street, City, State, Country}. This constraint may cause data redundancy. For example, "Mumbai" and "Goa" both the cities are in the "Maharashtra". The entries for such cities may cause data redundancy along the attributes State and Country.




SNOWFLAKE SCHEMA

NOTE − Due to normalization in the Snowflake schema, the redundancy is reduced and therefore, it becomes easy to maintain and the save storage space.




FACT CONSTELLATION SCHEMA




DIFFERENCE BETWEEN DATABASE and DATA WAREHOUSE

DATABASE

DATA WAREHOUSE




DATAGRID

A data grid is a set of structured services that provides multiple services like the ability to access, alter and transfer very large amounts of geographically separated data, especially for research and collaboration purposes. Data from different regions are pulled from administrative domains which filter data for security purposes, and present it to the user upon request by means of a middleware application.



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