Author: Zen and the Art of Computer Programming
1 Introduction
In 2021, the digital economy is booming, and a large amount of digital data is generated both online and offline. The value of data is gradually being recognized by more companies and driving business decisions. With the emergence of new management models such as "data governance" and "data empowerment", "data middle platform" has increasingly become the most important "cornerstone" in the digital transformation of enterprises. So, what kind of company is suitable as a data center, and how to build a service-oriented architecture for the data center? This article will explain the relevant knowledge from the following aspects, and demonstrate how to build a service-oriented architecture based on the data center through specific cases.
Data center overview
There are many definitions and connotations of Taiwan in data, but it is generally believed that it mainly includes the following points:
- Data center: an environment for data integration, storage, analysis, processing, mining, and application. The data center usually consists of a data warehouse, data lake, data wet station, and data pipeline;
- Data governance: including functional modules such as metadata management, data quality management, data security management, data value management, data use monitoring, data quality assurance, data sharing and discovery, etc., to support the needs of different types of data from different users and ensure that data subjects rights and interests are fully protected;
- Data empowerment: including data application development, tools, platforms, components, interfaces, etc., providing data development capabilities required by various business scenarios, which can improve the work efficiency of data scientists, engineers, analysts and business personnel, and promote the drive of data value force;
- Platform capabilities: including data analysis tools, development frameworks, data mining models, machine learning models, etc., which can support the entire life cycle of the data platform and provide flexibility and elasticity of data services;
- Connectivity capabilities: Including data fusion and integration capabilities of different data sources, different time zones, and different application scenarios, realizing automation, intelligence, and collaboration of data integration, and helping enterprises break through various business barriers.