ZT: Common Terms of Data Warehouse

ZT: Common Terms of Data Warehouse

Data warehouse in the middle of the 1980s S, "Father of Data Warehouse" William H. in his book "building a data warehouse", Mr. inmon defined the concept of a data warehouse, and then gave a more accurate definition: A data warehouse is a topic-oriented, integrated, time-related, and unchangeable data set in enterprise management and decision-making. Unlike other database applications, data warehouses are more like a process of integrating, processing, and analyzing business data distributed across the enterprise. Instead of a product that can be purchased. Data mart, or "Small Data Warehouse ". If the data warehouse is built on an enterprise-level data model. Data mart is a subset of enterprise-level data warehouses. It is mainly for department-level businesses and only for a specific topic. Data mart can alleviate the bottleneck of data warehouse access to a certain extent.

The concept of OLAP was first proposed by E. F. codd, the father of relational databases in 1993. At that time, codd believed that online transaction processing (OLTP) could not meet the needs of end users for database query and analysis, and SQL for simple queries on large databases could not meet the needs of user analysis. A user's decision analysis requires a large amount of computation on the relational database to obtain results. The query results cannot meet the requirements of decision makers. Therefore, codd proposes the concept of multi-dimensional database and multi-dimensional analysis, that is, OLAP.

Codd proposes 12 principles for OLAP to describe the OLAP system:

Criterion 1 the OLAP model must provide a multi-dimensional conceptual view
Guideline 2 transparency criteria
Criterion 3 estimation of access capability
Criterion 4 stable report capability
Guideline 5 customer/Server Architecture
Criterion 6-dimensional equality Criterion
Criterion 7 Dynamic sparse matrix processing Criterion
Criterion 8 multi-user support criteria
Criterion 9 unrestricted cross-dimensional operations
Guideline 10 intuitive data manipulation
Rule 11 flexible report generation
Rule 12 unrestricted dimension and clustering layers ROLAP

Based on the 12 codd standards, each software development vendor is wise and wise. One of the schools thinks that multi-dimensional data can be stored using relational databases. Therefore, A star schema is created based on the sparse matrix representation method. Later, the snowflake structure was evolved. To be different from multidimensional databases, relational database-based OLAP is called Relational OLAP (ROLAP. Representative Products include Informix metacube and Microsoft SQL Server OLAP services.

Molaparbor software strictly complies with the definition of codd and has established a multi-dimensional database to store online analysis system data. It pioneered multi-dimensional data storage, and many companies later adopted multi-dimensional data storage. Known as muiltdimension OLAP (molap for short), it indicates that the products include the company's (formerly known as Arbor software), the company's products, such as the data integration system, table store, table store, and table store. Client OLAP is relative to server OLAP. Some analysis tool manufacturers suggest downloading some data locally to provide users with local multi-dimensional analysis. Representative Products include brio designer and business object.

The DSS decision support system is equivalent to a data warehouse-based application. Decision-making support is to collect and process all relevant data and information to provide information for the decision-making management of enterprises and to provide a basis for decision-making by decision makers.

ETL data extraction (extract), transformation (Transform), cleaning, and load. An important part of building a data warehouse is that the user extracts the required data from the data source, cleans the data, and finally loads the data to the data warehouse according to the pre-defined data warehouse model.

Ad hoc query ad-hoc queries are the most common query of database applications. Using the data warehouse technology, users can obtain desired data from the database at any time.

The EIS lead Information System (Executive Information System) refers to the information query requirements of leaders who cannot focus on computer technology, A specially designed application that accesses a data warehouse through a simple graphical interface.

Business Process Reengineering (BPR) is a task that uses data warehouse technology to discover and correct the drawbacks of business processes. It is one of the important roles of data warehouses.

Bi Business Intelligence (BI) refers to the general name of warehouse-related technologies and applications. It refers to the use of various smart technologies to enhance the business competitiveness of enterprises.

Data Mining is a decision support process for data mining. It is mainly based on AI, machine learning, statistics, and other technologies. It is highly automated in analyzing the original data of enterprises and making disruptive reasoning, discover potential models, predict customer behaviors, help enterprise decision makers adjust market strategies, reduce risks, and make correct decisions.

Customer Relationship Management (CRM) is a new technology that is based on the database technology but fundamentally different from traditional database applications, CRM is a new application based on data warehouse technology. However, from the perspective of business operations, CRM is actually an old "application. For example, if a guest is an old customer of a hotel, the hotel will naturally know the habits and preferences of the guest, such as whether the hotel prefers to depend on the road, smoking, bed, breakfast, etc. When the guest is on demand again, the hotel will provide the room and service that the guest prefers without asking for help. This is a type of CRM.

Meta data metadata. Data in a data warehouse refers to the data source definitions, target definitions, conversion rules, and other key data generated during the data warehouse construction process. At the same time, metadata also contains commercial information about the meaning of data. All such information should be properly stored and managed. It facilitates the development and use of data warehouses.