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I would like to share with you what big data's four-tier stack technical architecture is. I believe most people don't know much about it, so share this article for your reference. I hope you will learn a lot after reading this article. let's learn about it!

Big data infrastructure is built as a stack technical architecture, including: 1, the basic layer. 2. Management layer; this layer includes not only data storage and management, but also data calculation. 3. Analysis layer; provide statistics-based data mining and machine learning algorithms for analyzing and interpreting data sets to help enterprises gain an in-depth understanding of the value of data. 4. Application layer.

Big data infrastructure is built as a stack technical architecture, including: basic layer, management layer, analysis layer, application layer.

Big data's four-tier stack technical architecture:

1. Basic layer

The first layer is not only the lowest level of the whole big data technology architecture, but also the basic layer. In order to realize the application of big data scale, enterprises need a highly automated and scalable storage and computing platform. This infrastructure needs to evolve from a previous storage island to a high-capacity storage pool with shared capacity. Capacity, performance, and throughput must be scalable linearly.

The cloud model encourages access to data and provides flexible resource pools to deal with large-scale problems, solving the problem of how to store large amounts of data and how to accumulate the necessary computing resources to manipulate the data. In the cloud, data is provisioned and distributed across multiple nodes, bringing the data closer to the users who need it, thus reducing response time and improving productivity.

2. Management

In order to support in-depth analysis of multi-source data, big data needs a management platform in the technical architecture to integrate structured and unstructured data management, with real-time transmission, query and computing functions. This layer includes not only data storage and management, but also data calculation. Parallelization and distribution are the elements that must be considered in big data's management platform.

3. Analysis layer

The application of big data needs the analysis of big data. The analysis layer provides statistics-based data mining and machine learning algorithms for analyzing and interpreting data sets to help enterprises gain a deep understanding of the value of the data. Big data analysis platform with strong expansibility and flexible use can become a sharp weapon for data scientists and get twice the result with half the effort.

4. Application layer

Big data's value is reflected in the application of helping enterprises make decisions and providing services for end users. Different new business needs drive the application of big data. On the contrary, the competitive advantage provided by big data application makes enterprises pay more attention to the value of big data. The new big data application constantly puts forward new requirements for big data technology, and big data technology is becoming more and more mature in the continuous development and changes.

These are all the contents of the article "what is big data's four-tier stack technical architecture". Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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