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As the name itself suggests that data warehouse is nothing but a central repository of all that data that can be used by different parts of the organization. In general, the repository can be physical or it can be logical as well. So the data warehousing focuses on the process of accumulating the data altogether and sees how that can be analyzed and accessed at a later point in time.
In the data warehousing concept, they are usually two approaches:
The name data warehousing is given by William H.Inmon, he is considered as Father of Data Warehousing. During this explanation about data warehousing, he specified that data warehousing is nothing but a
All of these factors support in terms of making decisions.
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The main difference between the data warehouse and the operational database is as follows:
A data warehouse is nothing but a collection of all the data that is related to an organization and this data can be used for the data analysis within the organization.
As the name itself is self-explanatory, all the data that is currently being used by the organization for transactional purposes can be considered as an operational database.
A data mart is nothing but an access layer of the data ware environment that is set up and it is widely used to get the data exported to the users. In a sense, data mart can be considered as the subset of the data that we already have in the data warehouse environment. Basically, a data warehouse has a whole chunk of data that is not tailored to a specific team or a department. With the help of data mart, the data can be tailored to a granular level where the information can be extracted and customized so that it can be useful information for a team within an organization.
A dimension can be defined as classification where it categorizes the measures and facts in an orderly fashion. Using these facts and measures, it will help the users to define and provide necessary answers for the business operations.
The common dimensions that are used are:
The primary functions of the dimensions are as follows:
Usually, these factors are all utilized in the concept of slicing and dicing the data. Out of which slicing refers to filtering the data and dicing the data refers to grouping the data.
The important responsibilities of warehouse manager are as follows:
The following are the bullet points for Query manager:
As the name implies, the query manager is responsible for all the user queries that are generated within the environment. Based on the queries used, the data is extracted.
The following are the bullet points of the Load manager, they are as follows:
The following are few things that can be expected from the load manager:
The following activities are involved in Data Warehouse:
The following are the benefits of the Data Warehouse implementation:
The term normalization is also considered as “Database Normalization”.
This is a process of rearranging or organizing the columns and the tables that are associated in a relational database. By doing this activity, reduces data redundancy and also helps in improving data integrity.
Further, this process also helps in simplifying the database design so that the optimal structure is enabled. In short, normalization helps the data to split into additional tables to incorporate the data and at the same time makes it easy while retrieving the data.
A fact table is nothing but a table that consists of information about measurements, facts, metrics of a business process. It is usually located in the center of a star schema. A star schema is also called a snowflake schema. Usually, a fact table consists of two types of columns:
There is only one fact table that is stored in the star schema or snowflake schema. So, multiple fact tables are stored under fact constellation schema.
The use of the normalization process, it helps in reducing data redundancy. It helps to maintain valid data that makes more sense to the users whenever it is needed.
The data marting is also called a “data mart”. A data mart is nothing but a process of redefining information about a specific data set that makes sense for a particular group.
The different kinds of costs associated with data marting are as follows:
DMQL stands for Data Mining Query Language. This language is used for schema definition. The language that is used in DMQL is nothing but SQL language. SQL stands for Structured Query Language.
A slice operation is nothing but a filtration process. So within this process, only one dimension is used in slice operation.
A dice operation is nothing but a grouping process, the data is grouped based on certain categories. So within this process, 2 or more dimensions are used in dice operation.
Data modeling is a process of representing the data view in the form of a graphical way. So within the data modeling process, the following activities are included:
The data warehouse modeling includes:
The key characteristics of a data warehouse are as follows:
Within a data warehousing environment, snowflaking is nothing but dimensional modeling. Within this multiple dimensions are stored in multiple related tables. A snowflake schema is one of the variations of a star schema.
The snowflake schema is used to improve the performance of the queries. The snowflaking concept is widely used in data warehouses and data marts to support a specific set of queries.