Prerequisite – Data Warehousing
Data warehouse can be controlled when the user has a shared way of explaining the trends that are introduced as specific subject. Below are major characteristics of data warehouse:
- Subject-oriented –
A data warehouse is always a subject oriented as it delivers information about a theme instead of organization’s current operations. It can be achieved on specific theme. That means the data warehousing process is proposed to handle with a specific theme which is more defined. These themes can be sales, distributions, marketing etc.
A data warehouse never put emphasis only current operations. Instead, it focuses on demonstrating and analysis of data to make various decision. It also delivers an easy and precise demonstration around particular theme by eliminating data which is not required to make the decisions.
- Integrated –
It is somewhere same as subject orientation which is made in a reliable format. Integration means founding a shared entity to scale the all similar data from the different databases. The data also required to be resided into various data warehouse in shared and generally granted manner.
A data warehouse is built by integrating data from various sources of data such that a mainframe and a relational database. In addition, it must have reliable naming conventions, format and codes. Integration of data warehouse benefits in effective analysis of data. Reliability in naming conventions, column scaling, encoding structure etc. should be confirmed. Integration of data warehouse handles various subject related warehouse.
- Time-Variant –
In this data is maintained via different intervals of time such as weekly, monthly, or annually etc. It founds various time limit which are structured between the large datasets and are held in online transaction process (OLTP). The time limits for data warehouse is wide-ranged than that of operational systems. The data resided in data warehouse is predictable with a specific interval of time and delivers information from the historical perspective. It comprises elements of time explicitly or implicitly. Another feature of time-variance is that once data is stored in the data warehouse then it cannot be modified, alter, or updated.
- Non-Volatile –
As the name defines the data resided in data warehouse is permanent. It also means that data is not erased or deleted when new data is inserted. It includes the mammoth quantity of data that is inserted into modification between the selected quantity on logical business. It evaluates the analysis within the technologies of warehouse.
In this, data is read-only and refreshed at particular intervals. This is beneficial in analysing historical data and in comprehension the functionality. It does not need transaction process, recapture and concurrency control mechanism. Functionalities such as delete, update, and insert that are done in an operational application are lost in data warehouse environment. Two types of data operations done in the data warehouse are:
- Data Loading
- Data Access
Functions of Data warehouse:
It works as a collection of data and here is organized by various communities that endures the features to recover the data functions. It has stocked facts about the tables which have high transaction levels which are observed so as to define the data warehousing techniques and major functions which are involved in this are mentioned below:
- Data consolidation
- Data Cleaning
- Data Integration
- SQL | Date functions
- SQL | Functions (Aggregate and Scalar Functions)
- Data Mining
- Data Warehousing
- Database Management System | Aggregate functions in SQL
- Data Abstraction and Data Independence
- DBMS | Data Replication
- Data Mining | Sources of Data that can be mined
- Data Mining | KDD process
- Data Warehouse Modeling | Snowflake Schema
- SQL | Difference between functions and stored procedures in PL/SQL
- DBMS | Row oriented vs. column oriented data stores
- Functions of Distributed Database System
- Star Schema in Data Warehouse modeling
- Dimensional Data Modeling
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