Database and Data Warehouse

Business intelligence is also known as Business Intelligence, the English language is Intelligence, abbreviated as BI.
The concept of business intelligence was first proposed by the Gartner Group in 1996 and the Gartner Group defined Business Intelligence as: Business Intelligence describes a range of concepts and methodologies to assist in the development of business decisions by applying a fact-based support system. Business intelligence provides technologies and methodologies that enable businesses to quickly analyze data, including collecting, managing, and analyzing data, translating that data into useful information, and distributing it throughout the enterprise.

Business intelligence is often understood as a tool to transform existing data in an enterprise into knowledge and to help companies make informed business decisions. The data discussed here include orders from Enterprise business Systems, inventory, transaction accounts, customer and supplier data from industry and competitors, and various data from other external environments where the business is located. Business intelligence can assist business decision-making, which can be either operational or tactical or strategic. To turn data into knowledge, you need to leverage technologies such as data warehousing, online analytical processing (OLAP) tools, and data mining. Therefore, from the technical level, business intelligence is not a new technology, it is only data warehousing, OLAP and data mining technology, such as the comprehensive use.

The realization of business intelligence involves software, hardware, consulting services and applications, and its basic architecture includes three parts: Data Warehouse, online analysis processing and data mining.

Therefore, it should be more appropriate to think of business intelligence as a solution. The key to business intelligence is to extract useful data from a number of data from different enterprise operating systems and clean it up to ensure the correctness of the data, then extract (Extraction), transform (transformation), and load (load), the ETL process, Merged into an enterprise-level data warehouse, so as to obtain a global view of enterprise data, based on the use of appropriate query and analysis tools, data mining tools, OLAP tools and other analysis and processing (this time information into the knowledge of auxiliary decision), and finally the knowledge presented to the manager, Provide support for the decision-making process of the manager.

Relational theory leads to relational database
Data Warehouse theory leads to data Warehouse

Any practical application has the corresponding theory as the support

In 1991, Bill Inmon, the father of the Data Warehouse, Bill Enmen the definition presented in the book "Building The Data Warehouse", the Data Warehouse, which is widely accepted- Warehouse) is a theme-oriented (Subject oriented), integrated (Integrated), relatively stable (non-volatile), data collection that reflects historical changes (time Variant) to support management decisions ( Decision Making Support).
The architecture of the Data Warehouse has two major schools of theoretical knowledge, which were presented by two Masters Ralph Kimball&bill Inmon in the early 90. These two masters are the leading gurus and theorists in the field of business intelligence/data warehousing, but their two-bit ideas and ideas differ greatly. Their followers also often have a debate about which architectures and ways to build better.
At home, we typically refer to the definition of the four characteristic angles of the Data warehouse (subject-oriented, integrated, relatively stable, reflecting historical changes to support decision-making), which is Inmon proposed, and he is also known as the father of data warehouses. And the Practice Master Kimball his Toolbox series of books, also is regarded as the Data Warehouse construction classic book.


From database to Data Warehouse

The difference between the two:
1, the starting point is different: The database is a transaction-oriented design, the Data Warehouse is a theme-oriented design.
2, the stored data is different: The database generally stores online transaction data; The Data warehouse is generally stored in historical data.
3, the design rules are different: The database design is to avoid redundancy as far as possible, generally adopt the rules to conform to the paradigm to design; Data Warehouse design is intentionally introduced redundancy, the use of anti-paradigm approach.
4, the provision of different functions: The database is designed to capture data, the Data Warehouse is designed for the analysis of data,
5, the basic elements are different: the basic elements of the database is the fact table, the basic element of the data warehouse is the dimension table.
6, the capacity is different: the database in the basic capacity is much smaller than the Data warehouse.
7, the service object is different: The database is designed for efficient transaction processing, the service object is the staff of enterprise business processing; The Data Warehouse is designed to analyze the data for decision-making, and the service object is the senior decision-maker of the enterprise.


Business data processing is broadly divided into two categories:
One kind is the operation type processing, also called the online transaction processing, it is for the specific business in the database online daily operation, usually carries on the query to the few records, modifies.
The other is analytical processing, which is generally based on the historical data of some topics and supports management decisions. Data warehousing, data mining
OLTP Two-dimensional relational online transaction processing on-line transaction processing
OLAP Multidimensional Relational online analysis processing on-line Analytical Processing Data Warehouse, data mining
OLTP is the main application of the traditional relational database, mainly basic, daily transaction processing, such as bank transaction.
OLAP is the main application of Data Warehouse system, supports complex analysis operations, focuses on decision support, and provides intuitive and understandable query results.

The concept of online analytical processing (OLAP) was first proposed by the parent of the relational database e.f.codd in 1993, and he also proposed 12 guidelines on OLAP. OLAP has aroused a great deal of repercussions, OLAP as a class of products and online transaction processing (OLTP) clearly distinguished.

	Oltp	Olap
User	Operators, low-level management staff	Decision makers, senior managers
Function	Daily operation Processing	Analysis decisions
DB Design	Application oriented	Topic oriented
Data	The current, the newest detail, the two-dimensional discrete	A. of historical, aggregated, multidimensional, integrated, unified
Access	Read/write dozens of records	Read the millions record.
Work unit	A simple transaction	Complex queries
DB size	100mb-gb	100gb-tb

Database and Data Warehouse