Big data, business intelligence, online analytical processing, data mining, busi
ID: 3911078 • Letter: B
Question
Big data, business intelligence, online analytical processing, data mining, business analytics all have one thing in common; they often rely on a data warehouse to store information. Data warehouses store very large amounts of data. This raw data is of little value unless it can be transformed into meaningful and useful information. In this application, you will use the text and other resources to examine data warehousing. You will submit a 3-4 page paper that addresses the following topics: .What is a data warehouse? What are the primary similarities and differences between a data warehouse and a transactional database? When should an organization consider developing a data warehouse? How are data warehouses structured? Describe at least two real-world examples of how a data warehouse is used to provide "business intelligence" that can give an organization a competitive advantageExplanation / Answer
Answer)
Data WareHouse :
The data warehouse which is otherwise known as the enterprise data warehouse is defined as a system meant for reporting as well as analysis of data which is referred as an important component for the business intelligence.
These are indeed the central repository which contains the data that are integrated from among the different sources of data and are being stored in one place which help in creating the analytical reports for the workers in the enterprise.
The similiarity is that they both are responsible for online transaction processing of data.
Which is both the OLAP and OLTP systems do store as well as manage the data in the form of tables, columns, indexes , keys, views, as well as data types. And also both of these use the SQL for querying the data.
Where as the difference being in database one would only store the data where as in data warehouse one would analyze the data which is being stored.
A dataware house is a layer above the database which is meant for optimization and is meant for analytics.
Database is not structured for analysis of data where as data ware house is.
The SLA's for OLTP is 99.99% where as OLAP is flexible.
OLAP has an effective data structure and is organized for facilitating the reporting as well as analysis.
An organization should consider developing a warehouse when there are different copies of the data from among different sources for using it for analysis.
When one data would be in infinite applications and databases.
Where the SLA can be flexible and where optimization is effectively needed.
The data ware house is structured as an OLAP Database which is a layer on top of the OLTP database and are strutured effectively for data optimization so as to use the data for analysis from among different sources.
Examples are as follows:
Cash Register - This is the company's point of sale .
The office computer that we use also contain the data which are optimizzed.
The two additional facts which one can discover are as follows:
1) All the OLAP databases are not similar
2) Maximum data warehouses are either dimensional or enterprised data.
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