What do data warehouses support

Earlier, organizations started relatively simple use of data warehousing. However, over time, more sophisticated use of data warehousing begun. In this stage, data is just copied from an operational system to another server. Data in the Datawarehouse is regularly updated from the Operational Database. The data in Datawarehouse is mapped and transformed to meet the Datawarehouse objectives.

In this stage, Data warehouses are updated whenever any transaction takes place in operational database. For example, Airline or railway booking system. In this stage, Data Warehouses are updated continuously when the operational system performs a transaction.

The Datawarehouse then generates transactions which are passed back to the operational system. Load manager: Load manager is also called the front component. It performs with all the operations associated with the extraction and load of data into the warehouse. These operations include transformations to prepare the data for entering into the Data warehouse. Warehouse Manager: Warehouse manager performs operations associated with the management of the data in the warehouse. It performs operations like analysis of data to ensure consistency, creation of indexes and views, generation of denormalization and aggregations, transformation and merging of source data and archiving and baking-up data.

Query Manager: Query manager is also known as backend component. It performs all the operation operations related to the management of user queries. The operations of this Data warehouse components are direct queries to the appropriate tables for scheduling the execution of queries. This is categorized into five different groups like 1.

Data Reporting 2. Query Tools 3. Application development tools 4. EIS tools, 5. OLAP tools and data mining tools. By contrast, a data warehouse stores data in files or folders in a more organized fashion that is readily available for reporting and data analysis. Data warehouses are also sometimes confused with data marts.

But data warehouses are generally much bigger and contain a greater variety of data, while data marts are limited in their application. Data marts are often subsets of a warehouse, designed to easily deliver specific data to a specific user, for a specific application. In the simplest terms, data marts can be thought of as single-subject, while data warehouses cover multiple subjects.

As businesses make the move to the cloud , so too do their databases and data warehousing tools. The cloud offers many advantages: flexibility, collaboration, and accessibility from anywhere, to name a few. The data in a data warehouse provides information from the historical point of view.

A data warehouse is kept separate from the operational database and therefore frequent changes in operational database is not reflected in the data warehouse.

As discussed before, a data warehouse helps business executives to organize, analyze, and use their data for decision making. A data warehouse serves as a sole part of a plan-execute-assess "closed-loop" feedback system for the enterprise management.

The data can be processed by means of querying, basic statistical analysis, reporting using crosstabs, tables, charts, or graphs. The data can be analyzed by means of basic OLAP operations, including slice-and-dice, drill down, drill up, and pivoting. These mining results can be presented using the visualization tools. Data Warehousing - Overview Advertisements. Factor analysis B. Regression analysis C. Data mining D.

Data scrapping E. Data cloning Choose the most appropriate answer from the options given below:. An enormous collection of data on various topics from a variety of internal and external sources, compiled by a firm for its own use or for use by its clients, is called :.

What do data warehouses support? Which one of these is characteristic of RAID 5? Suggested Test Series. Suggested Exams.