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This article introduces you how to understand the big data system architecture. The content is very detailed. Interested friends can refer to it for reference. I hope it can help you.

The application development of big data is too biased towards the bottom layer, which has the problems of high learning difficulty and wide technical coverage, which restricts the popularization of big data. Now we need a technology to encapsulate some common and reused basic codes and algorithms in big data development into class libraries, reduce the learning threshold of big data, reduce the difficulty of development, and improve the development efficiency of big data projects.

There are three applications of big data in work: business-related, such as user portraits, risk control, etc.;

Decision-related, the field of data science, understanding statistics, algorithms, which is the domain of data scientists; engineering-related, how to implement, how to implement, what business problems to solve, which is the job of data engineers.

The characteristics of the data source determine the technical selection of data acquisition and data storage. I divide them into four categories according to the characteristics of the data source:

The first category is divided into internal data and external data from the source point of view;

The second category: divided into unstructured data and structured data from the structural point of view;

The third category: from the point of view of variability, it can be divided into immutable data that can be added and modified data that can be deleted;

The fourth category is divided into large amounts of data and small amounts of data in terms of scale.

The first element of the big data platform is the data source. The data source we want to process is often on the business system. When analyzing the data, we may not directly process the data source of the business, but first through data collection and data storage, and then through data analysis and data processing.

As you can see from the whole large ecosystem, it takes a lot of resources to complete data engineering; the large amount of data requires clustering; to control and coordinate these resources requires monitoring and coordination; how to deploy large-scale data is easier and easier; and it involves logging, security, and possibly integration with the cloud. These are all edges of the big data circle, which are equally important.

DKH is a one-stop search engine level and big data general computing platform designed by DKH in order to open up the channel between big data ecosystem and traditional non-big data companies. By using DKH, traditional companies can easily cross the big data technology gap and achieve search engine level big data platform performance.

DKH effectively integrates all components of the entire HADOOP ecosystem, and is deeply optimized and recompiled into a complete higher-performance big data general computing platform, realizing the organic coordination of various components. Therefore, DKH has up to 5 times (maximum) performance improvement in computing performance compared to open source big data platforms.

DKH simplifies complex big data cluster configuration to three types of nodes (master node, management node and compute node) through DKH's unique middleware technology, greatly simplifying cluster management and operation, and enhancing cluster high availability, high maintainability and high stability.

Although DKH is highly integrated, it still maintains all the advantages of open source systems and is 100% compatible with open source systems. Big data applications developed on open source platforms can run efficiently on DKH without any changes, and the performance will be improved by up to 5 times.

DKH integrates the big data integrated development framework (FreeRCH). FreeRCH provides more than 20 classes commonly used in big data, search, natural language processing and artificial intelligence development. Through a total of more than 100 methods, it has achieved more than 10 times improvement in development efficiency.

The SQL version of DKH also provides integration of distributed MySQL, traditional information systems, and seamless implementation of big data-oriented and distributed spanning.

Technical framework diagram of DKH standard platform

How to understand the architecture of big data system is shared here. I hope the above content can be of some help to everyone and learn more knowledge. If you think the article is good, you can share it so that more people can see it.

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