Shulou(Shulou.com)06/03 Report--

In this issue, the editor will bring you about the connections and differences between database and data warehouse. The article is rich in content and analyzed and described from a professional point of view. I hope you can get something after reading this article.

Background

In the two years of contact with big data, the word data warehouse frequently appeared in my ears. With the gradual accumulation of knowledge, I also have a general understanding of data warehouse and database.

Concept

Database (DB)

To put it simply, a database is a warehouse for storing data, which mainly includes relational database and non-relational database, which is a logical concept.

Data Warehouse (DW)

Logically, consistent with the concept of the database, it is a warehouse for storing data, but the amount of data in the data warehouse is larger.

Characteristics of data Warehouse

Data warehouse is topic-oriented.

So what is a theme? in a nutshell, a theme is what users care about when using a data warehouse.

Data warehouse does not support modification, which is understandable. Unlike databases, data warehouses do not support update and delete operations.

The data in the data warehouse changes over time, which does not conflict with the previous item. This change does not refer to the changes caused by update or deletes, but the constant addition of new content or deletion of old content over time.

Data warehouse is the integration of multiple heterogeneous data sources.

The data warehouse generally stores historical data.

The data warehouse is weakly transactional because it stores historical data and generally reads (analyzes) data scenarios.

The database is designed to capture data, and the data warehouse is designed to analyze data.

The difference between database and data warehouse

The database is designed to capture data, and the data warehouse is designed to analyze data. The difference between database and data warehouse is essentially the difference between OLTP and OLAP.

OLTP: online transaction processing (On-Line Transaction Processing) is the application of traditional relational database.

OLTP is user-oriented transaction processing for programmers and query processing for customers.

OLTP system emphasizes the efficiency of database memory, the command rate of various indicators of memory, binding variables, and parallel operations. Users are more concerned about the response time of operations, data security, integrity and the number of concurrent users.

In order to ensure atomicity, the access of OLTP system has transaction mechanism and recovery mechanism. It is mainly used for data capture.

OLAP: online Analytical transaction processing (On-Line Analytical Processing)

OLAP is market oriented and is used for data analysis of knowledge workers (managers, supervisors, and data analysts).

OLAP usually integrates a large amount of data from multiple heterogeneous data sources.

It is the main application of data warehouse system, supporting complex analysis operations, focusing on decision support, and providing intuitive and easy-to-understand query results.

On the other hand, the OLAP system emphasizes data analysis, SQL execution market, disk Icano, partition and so on. Mainly used for data analysis.

OLAP systems generally store historical data, so most of them are read-only operations and do not require transactions.

These are the connections and differences between the database and the data warehouse shared by the editor. If you happen to have similar doubts, please refer to the above analysis to understand. If you want to know more about it, you are welcome to follow the industry information channel.

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