The big data infrastructure is built as a stack technology architecture, including: 1. The base layer, which is the bottom layer of the entire big data technology architecture; 2. The management layer, which includes both data storage and management, and Calculation of data; 3. Analysis layer, which provides statistics-based data mining and machine learning algorithms for analyzing and interpreting data sets, helping enterprises gain an in-depth understanding of the value of data; 4. Application layer, providing enterprises with competitive advantages This makes enterprises pay more attention to the value of big data.
The operating environment of this tutorial: Windows 7 system, Dell G3 computer.
The big data infrastructure is built as a stack technology architecture, including: base layer, management layer, analysis layer, and application layer.
The four-layer stack technology architecture of big data:
1. Basic layer
The first layer serves as the foundation of the entire big data technology architecture. The bottom layer is also the basic layer. To achieve big data-scale applications, enterprises need a highly automated, horizontally scalable storage and computing platform. This infrastructure needs to evolve from former storage silos to high-capacity storage pools with shared capabilities. Capacity, performance and throughput must be linearly scalable.
The cloud model encourages access to data and provides an elastic resource pool to deal with large-scale problems, solving the problem of how to store large amounts of data and how to accumulate the required computing resources to operate the data. In the cloud, data is provisioned and distributed across multiple nodes, bringing data closer to the users who need it, resulting in faster response times and increased productivity.
2. Management
To support in-depth analysis on multi-source data, a management platform is needed in the big data technology architecture to integrate structured and unstructured data management. , with real-time transmission, query, and calculation functions. This layer includes both data storage and management, and data calculation. Parallelization and distribution are elements that must be considered in a big data management platform.
3. Analysis layer
Big data applications require big data analysis. The analysis layer provides statistics-based data mining and machine learning algorithms for analyzing and interpreting data sets, helping enterprises gain in-depth insights into the value of data. A big data analysis platform with strong scalability and flexible use can become a powerful tool for data scientists, achieving twice the result with half the effort.
4. Application layer
The value of big data is reflected in the applications that help enterprises make decisions and provide services to end users. Different new business needs drive the application of big data. On the contrary, the competitive advantages provided by big data applications to enterprises make enterprises pay more attention to the value of big data. New big data applications continue to put forward new requirements for big data technology, and big data technology is therefore becoming increasingly mature amid constant development and changes.
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