What are the plans of big data engineers for data platform construction
[Introduction] Data platforms actually exist in the process of enterprise development. In the era of big data with explosive growth of data, traditional enterprise-level databases cannot fully satisfy various requirements in data management applications. As far as the enterprise itself is concerned, there is a need for a data platform construction plan that is more in line with the demand. Then, what are the plans for big data engineers to construct the data platform? Let's take a closer look.
1. Agile data mart
Data marts are also a common solution. The underlying data products are bound to the analysis layer, so that the application layer can directly perform drag-and-drop analysis on the data in the underlying data products. The main advantage of data marts lies in the simple and rapid integration of business data, the realization of agile modeling, and the significant increase in data processing speed.
2. Conventional data warehouse
The focus of the data warehouse is to integrate data and also to sort out business logic. Although the data warehouse can also be packaged into something like SAAS Cube to improve data reading performance, the role of the data warehouse is more to solve the company's business problems.
3. Hadoop distributed system architecture
Of course, for large-scale distributed system architecture, Hadoop still stands in an irreplaceable key position. Major domestic and foreign companies such as Yahoo, Facebook, Baidu, Taobao, etc., were initially based on Hadoop.
The Hadoop ecosystem is huge, and the needs that enterprises can achieve based on Hadoop are not 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.
4. MPP (Massively Parallel Processing) architecture
Since entering the era of big data, the traditional mainframe computing model can no longer meet the demand. Distributed storage and distributed computing are king. The familiar Hadoop MapReduce framework and MPP computing framework are all based on this background.
The representative product of the MPP architecture is Greenplum. Greenplum's database engine is based on Postgresql, and through the Interconnnect artifact, efficient collaboration and parallel computing of multiple Postgresql instances in the same cluster are realized.
As for the relevant content of the big data engineer’s data platform construction plan, I will introduce to you all here. Since the development of Chinese society, the application of big data is gradually popularizing, so the future prospects are immeasurable. I hope that those who want to engage in this industry can make a reasonable choice .