The analytics layer of big data technology architecture provides statistically based data.

The four-layer stacked technology architecture of Big Data:

1. Foundation Layer

The first layer serves as the bottom layer of the entire Big Data technology architecture foundation, and is also the foundation layer. To realize big data-scale applications, enterprises need a highly automated, horizontally scalable storage and compute platform. This infrastructure needs to evolve from previous storage silos to high-capacity storage pools with ****-enjoyment capabilities. Capacity, performance and throughput must be linearly scalable.

The cloud model encourages access to data and provides an elastic pool of resources to cope with large-scale problems, solving the problem of how to store large amounts of data and how to accumulate the computing resources needed to manipulate it. In the cloud, data is provisioned and distributed across multiple nodes, bringing it closer to the users who need it, allowing for shorter response times and increased productivity.

2. Management layer

To support deep analysis on multi-source data, a management platform is needed in the big data technology architecture to enable structured and unstructured data management as a whole with real-time delivery and query and calculation functions. This layer involves both data storage and management and data computation. Parallelization and distribution are elements that must be considered for a big data management platform.

3. Analytics Layer

Big data applications require big data analysis. The analytics layer provides statistically based data mining and machine learning algorithms for analyzing and interpreting datasets to help companies gain an in-depth appreciation of the value of their data. Big data analytics platforms that are highly scalable and flexible in use can be even more of a tool for data scientists, yielding twice the results 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. Conversely, the competitive advantage that big data applications provide to enterprises makes them pay more attention to the value of big data. New big data applications are constantly putting forward new requirements for big data technologies, and big data technologies are therefore maturing in the midst of continuous development and change.