Data Warehousing

Introduction : Basic Concepts

A Data Warehouse is Built by combining data from multiple diverse sources that support analytical reporting, structured and unstructured queries, and decision making for the organization, and Data Warehousing is a step-by-step approach for constructing and using a Data Warehouse. Many data scientists get their data in raw formats from various sources of data and information. But, for many data scientists also as business decision-makers, particularly in big enterprises, the main sources of data and information are corporate data warehouses. A data warehouse holds data from multiple sources, including internal databases and Software (SaaS) platforms. After the data is loaded, it often cleansed, transformed, and checked for quality before it is used for analytics reporting, data science, machine learning, or anything.

What is Data Warehouse?

A Data Warehouse is a collection of software tools that facilitates analysis ofa large set of business data used to help an organization make decisions. A large amount of data in data warehouses comes from numerous sources such that internal applications like marketing, sales, and finance; customer-facing apps; and external partner systems, among others. It is a centralized data repository for analysts that can be queried whenever required for business benefits. A data warehouse is mainly a data management system that’s designed to enable and support business intelligence (BI) activities, particularly analytics. Data warehouses are alleged to perform queries, cleaning, manipulating, transforming and analyzing the data and they also contain large amounts of historical data.

What is Data Warehousing?

The process of creating data warehouses to store a large amount of data is named Data Warehousing. Data Warehousing helps to improve the speed and efficiency of accessing different data sets and makes it easier for company decision-makers to obtain insights that will help the business and promoting marketing tactics that set them aside from their competitors. We can say that it is a blend of technologies and components which aids the strategic use of data and information. The main goal of data warehousing is to create a hoarded wealth of historical data that can be retrieved and analyzed to supply helpful insight into the organization’s operations.

Need of Data Warehousing.

Data Warehousing is a progressively essential tool for business intelligence. It allows organizations to make quality business decisions. The data warehouse benefits by improving data analytics, it also helps to gain considerable revenue and the strength to compete more strategically in the market. By efficiently providing systematic, contextual data to the business intelligence tool of an organization, the data warehouses can find out more practical business strategies.

1. Business User: Business users or customers need a data warehouse to look at summarized data from the past.
   Since these people are coming from a non-technical background also, the data may be represented to them in an uncomplicated way.
2. Maintains consistency: Data warehouses are programmed in such a way that they can be applied in a regular format
   to all collected data from different sources, which makes it effortless
   for company decision-makers to analyze and share data insights with their
   colleagues around the globe. By standardizing the data, the risk of error
   in interpretation is also reduced and improves overall accuracy.
3. Store historical data: Data
   Warehouses are also used to store historical data that means, the time variable
   data from the past and this input can be used for various purposes.
4. Make strategic decisions: Data warehouses contribute to making better strategic decisions. Some business
   strategies may be depending upon the data stored within the data
   warehouses.
5. High response time: Data warehouse has got to be prepared for somewhat sudden masses and type of queries that
   demands a major degree of flexibility and fast latency.

Characteristics of Data warehouse:

1. Subject Oriented: A data warehouse is often subject-oriented because it delivers may be achieved on a
   particular theme which means the data warehousing process is proposed to handle a particular theme that is more defined. These themes are often sales, distribution, selling. etc.
2. Time-Variant: When the data is maintained via totally different intervals of time like weekly, monthly,
   or annually, etc. It founds numerous time limits that are unit structured between the big datasets and are command within the online transaction method (OLTP). The time limits for the data warehouse are extended than that of operational systems. The data resided within the data warehouse is predetermined with a particular interval of time and delivers information from the historical perspective. It contains parts of time directly or indirectly.
3. Non-volatile: The data residing in the data warehouse is permanent and defined by its names. It additionally
   means that the data in the data warehouse is cannot be erased or deleted
   or also when new data is inserted into it. In the data warehouse, data is
   read-only and can only be refreshed at a particular interval of time.
   Operations such as delete, update and insert that is done in a software
   application over data is lost in the data warehouse environment. There
   are only two types of data operations that can be done in the data
   warehouse:
4.  

• Data Loading
• Data Access
• Integrated: A data warehouse is created by integrating data from numerous different sources such that from mainframe computers and a relational database. Additionally, it should also have reliable naming conventions, formats, and codes. Integration of data warehouse benefits in the successful analysis of data. Dependability in naming conventions, column scaling, encoding structure, etc. needs to be confirmed. Integration of data warehouse handles numerous subject-oriented
  warehouses.