1, conventional data warehouse

The focus of the data warehouse, is the integration of data, but also a sorting out of business logic. Although the data warehouse can also be packaged into a SAAS kind of Cube kind of thing to improve the performance of reading data, but the role of the data warehouse, more in order to solve the company's business problems.

2, agile data mart

Data mart is also a common scenario, the underlying data products and analytics layer binding, so that the application layer can be directly drag and drop analysis of data in the underlying data products. Data marts, the main advantage is a simple, fast integration of business data, to achieve agile modeling, and significantly improve the processing speed of data.

3, MPP (massively parallel processing) architecture

Entering the era of big data, the traditional mainframe computing model has not been able to meet the demand for distributed storage and distributed computing is the king. Everyone is familiar with the Hadoop MapReduce framework and MPP computing framework, are based on this background.

The representative product of the MPP architecture is Greenplum, whose database engine is based on Postgresql and realizes efficient collaboration and parallel computation of multiple Postgresql instances in the same cluster through the Interconnnect artifact.

4, Hadoop distributed system architecture

Of course, the large-scale distributed system architecture, Hadoop is still standing in an irreplaceable key position. Yahoo, Facebook, Baidu, Taobao and other large enterprises at home and abroad, initially based on Hadoop to start.

The Hadoop ecosystem is huge, and the needs that enterprises can realize based on Hadoop are not only limited to data analysis, but also include machine learning, data mining, and real-time systems. Enterprises build big data system platforms, Hadoop's big data processing capabilities, high reliability, high fault tolerance, open source, and low cost, make it the first choice.

On what kinds of programs are available for data platform construction, Global Green Ivy will share with you here. If you have a strong interest in big data engineering, I hope this article can help you. If you still want to know more about data analysts, big data engineers tips and materials, you can click on other articles on this site to learn.